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UAV-Aided data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint

arXiv

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arXiv 2025年

作者： Shao, Shuo Zhu, Haozhe Yao, Hongwei Li, Yiming Zhang, Tianwei Qin, Zhan Ren, Kui State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Department of Computer Science City University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models by preventing their unauthorized reuse. It is promising and convenient since it does not necessitate modifying the protected model. In this paper, we revisit existing fingerprinting methods and reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model. We demonstrate that this vulnerability mostly stems from their untargeted nature, where they generally compare the outputs of given samples on different models instead of the similarities to specific references. Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks. Specifically, FIT-Print transforms the fingerprint into a targeted signature via optimization. Building on the principles of FIT-Print, we develop bit-wise and list-wise black-box model fingerprinting methods, i.e., FIT-ModelDiff and FIT-LIME, which exploit the distance between model outputs and the feature attribution of specific samples as the fingerprint, respectively. Extensive experiments on benchmark models and datasets verify the effectiveness, conferrability, and resistance to false claim attacks of our FIT-Print. © 2025, CC BY.

关键词： Open systems

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HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract)

HAM: Hybrid Associations Models for Sequential Recommendatio...

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International Conference on data Engineering

作者： Bo Peng Zhiyun Ren Srinivasan Parthasarathy Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Biomedical Informatics The Ohio State University Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University Department of Biomedical Informatics Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University

Sequential recommendation aims to identify and recommend the next few items of users’ interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data Engineering. Please refer to the full manuscript [4] for more details.

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Multi-Representation Genetic programming: A Case Study on Tree-based and Linear Representations

arXiv

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arXiv 2024年

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Banzhaf, Wolfgang Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Computer Science and Engineering BEACON Center for the Study of Evolution in Action and Ecology Evolution and Behavior Program Michigan State University East LansingMI48864 United States

Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various pros and cons in different domains. However, due to the complicated relationships among representation and fitness landscapes of GP, it is hard to intuitively determine which GP representation is the most suitable for solving a certain problem. Evolving programs (or models) with multiple representations simultaneously can alternatively search on different fitness landscapes since representations are highly related to the search space that essentially defines the fitness landscape. Fully using the latent synergies among different GP individual representations might be helpful for GP to search for better solutions. However, existing GP literature rarely investigates the simultaneous effective use of evolving multiple representations. To fill this gap, this paper proposes a multi-representation GP algorithm based on tree-based and linear representations, which are two commonly used GP representations. In addition, we develop a new cross-representation crossover operator to harness the interplay between tree-based and linear representations. Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP with solely tree-based or linear representation in solving symbolic regression and dynamic job shop scheduling problems. © 2024, CC BY.

关键词： Genetic programming

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FINITE-TIME ANALYSIS OF ON-POLICY HETEROGENEOUS FEDERATED REINFORCEMENT LEARNING 12

FINITE-TIME ANALYSIS OF ON-POLICY HETEROGENEOUS FEDERATED RE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Chenyu Wang, Han Mitra, Aritra Anderson, James Data Science Institute Columbia University New YorkNY10025 United States Department of Electrical Engineering Columbia University New YorkNY10025 United States Department of Electrical and Computer Engineering NC State University RaleighNC27695 United States

Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing the sample complexity of reinforcement learning tasks by exploiting information from different agents. However, when each agent interacts with a potentially different environment, little to nothing is known theoretically about the non-asymptotic performance of FRL algorithms. The lack of such results can be attributed to various technical challenges and their intricate interplay: Markovian sampling, linear function approximation, multiple local updates to save communication, heterogeneity in the reward functions and transition kernels of the agents' MDPs, and continuous state-action spaces. Moreover, in the on-policy setting, the behavior policies vary with time, further complicating the analysis. In response, we introduce FedSARSA, a novel federated on-policy reinforcement learning scheme, equipped with linear function approximation, to address these challenges and provide a comprehensive finite-time error analysis. Notably, we establish that FedSARSA converges to a policy that is near-optimal for all agents, with the extent of near-optimality proportional to the level of heterogeneity. Furthermore, we prove that FedSARSA leverages agent collaboration to enable linear speedups as the number of agents increases, which holds for both fixed and adaptive step-size configurations. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Enhancing Histopathological Image Analysis: A Study on Effect of Color Normalization and Activation Functions 1st

Enhancing Histopathological Image Analysis: A Study on Effe...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Sudhamsh, G.V.S. Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Histopathological images have significant potential for disease diagnosis and prognosis, but their inherent color variations can impede accurate analysis and reliable model performance. Color variations in histopathological images can indeed be a major challenge for accurate diagnosis and reliable model performance. This study investigates the critical role of color normalization in mitigating these inconsistencies, improving feature extraction, the model’s generalizability, and reducing staining bias. More this study examines the impact of activation functions, essential components in deep learning architectures for histopathological image analysis. Selecting the optimal function is crucial for optimizing the model performance. By emphasizing the importance of color normalization and careful activation function selection, this study lays the groundwork for more robust and reliable analysis of histopathological images, ultimately leading to improved disease diagnosis. In the present work, different segmentation architectures are analyzed with and without color normalization and with different activation functions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models

arXiv

引用

arXiv 2024年

作者： Villena, Fabián Miranda, Luis Aracena, Claudio Department of Computer Science University of Chile Chile Millennium Institute for Foundational Research on Data Chile Department of Computer Science Catholic University of Chile Chile Faculty of Physical and Mathematical Sciences University of Chile Chile

Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs;by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library’s flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

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Cognitive Detection of Anomalies in Autonomous In-Vehicle Network Communication

Cognitive Detection of Anomalies in Autonomous In-Vehicle Ne...

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Advancements in Power, Communication and Intelligent Systems (APCI), International Conference on

作者： Suzen Firasta Yash Rahul Srivastava Vidya Rao Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350363289

ISBN: (纸本)9798350363296

The research addresses the critical issue of detecting anomalies in autonomous in-vehicle network communication, focusing on the vulnerability of infotainment systems to denial-of-service attacks. The aim is to explore and compare traditional Machine Learning (ML) and advanced Deep Learning (DL) models for the detection of DoS attacks within Controller Area Network (CAN) communications. By evaluating the performance of various ML and DL algorithms, including Decision Tree, Logistic Regression, RandomForest, Feed Forward Neural Networks, and LSTM architectures, the study aims to enhance intrusion detection capabilities in vehicular communication systems. The significance of the research lies in identifying the advantages of DL methods in capturing complex patterns and temporal dependencies within the CAN network, thus contributing to fortifying the security of automotive networks against evolving threats. The results demonstrate the efficacy of the proposed models in detecting and mitigating DoS attacks, with DL methods showing promise in automatic learning and adaptability to intricate network structures. This study provides valuable insights for future research endeavors aimed at enhancing the cybersecurity of autonomous vehicles.

关键词： Deep learning Adaptation models Soft sensors Prevention and mitigation Intrusion detection Training data Denial-of-service attack

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm

A Novel Approach for Heart Disease Classification using Casc...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Rajitha. M S. Santhana Megala School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer Technology Hindustan College of Arts and Science Coimbatore India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes.

关键词： Heart Support vector machines Accuracy Machine learning algorithms Computational modeling Boosting Classification algorithms Medical diagnosis Standards Diseases

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