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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University JD Explore Academy Department of Automation University of Science and Technology of China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Department of Computer Science Hong Kong Baptist University

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

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Secure communication of Active RIS Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Li, Xuehua Pei, Yingjie Yue, Xinwei Liu, Yuanwei Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA);ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP;and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Security Decision Support in the Control systems based on Graph Models 4

Security Decision Support in the Control Systems based on Gr...

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4th International Conference on Control in Technical systems, CTS 2021

作者： Doynikova, Elena V. Fedorchenko, Andrei V. Novikova, Evgenia S. Ushakov, Igor A. Krasov, Andrey V. Department of Secure Communication Systems The Bonch-Bruevich Saint Petersburg State University of Telecommunications St. Petersburg Russia St. Petersburg Russia Department of Computer Science and Engineering St. Petersburg Electrotechnical University "LETI" St. Petersburg Russia Department of Secure Communication Systems The Bonch-Bruevich Saint Petersburg State University of Telecommunications St. Petersburg Russia

ISBN: (纸本)9781665424547

An effective response against information security violations in the technical systems remains relevant challenge nowadays, when their number, complexity, and the level of possible losses are growing. The violation can be caused by the set of the intruder’s consistent actions. In the area of countermeasure selection for a proactive and reactive response against security violations, there are a large number of techniques. The techniques based on graph models seem to be promising. These models allow representing the set of actions caused the violation. Their advantages include the ability to forecast violations for timely decision-making on the countermeasures, as well as the ability to analyze and consider the coverage of countermeasures in terms of steps caused the violation. The paper proposes and describes a decision support method for responding against information security violations in the technical systems based on the graph models, as well as the developed models, including the countermeasure model and the graph representing the set of actions caused the information security violation. © 2021 IEEE.

关键词： Security of data

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Active Weighted Aging Ensemble for Drifted Data Stream

SSRN

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SSRN 2022年

作者： Woźniak, Michal Zyblewski, Pawel Ksieniewicz, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeźe Wyspiańskiego 27 Wroclaw50-370 Poland

One of the significant problems in streaming data classification is the concept drift phenomenon, which consists of the change in probabilistic characteristics of the classification task. Such changes in posterior probability destabilize the classification model performance, seriously degrading its quality. It is necessary to design appropriate strategies to counteract this phenomenon, allowing the classifier to adapt to the changing probabilistic characteristics. Especially difficult is to propose such an approach with the limited access to data labels. A human bias of high quality is usually costly, so to minimize the expenses related to this process, it is also necessary to propose learning strategies based on semi-supervised learning. Such strategies employ active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier’s performance. This paper proposes a novel chunk-based method for non-stationary data streams based on ensemble and active learning paradigms, considering a limited budget that may be applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using real and synthetic data streams, which confirm the high quality of the proposed algorithm over state-of-the-art methods. © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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Using GPT-4 to guide causal machine learning

arXiv

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arXiv 2024年

作者： Constantinou, Anthony C. Kitson, Neville K. Zanga, Alessio research group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Informatics Systems and Communication University of Milano - Bicocca Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland

Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLMs). In this paper, we are interested in the ability of LLMs to identify causal relationships. We focus on the well-established GPT-4 (Turbo) and evaluate its performance under the most restrictive conditions, by isolating its ability to infer causal relationships based solely on the variable labels without being given any other context by humans, demonstrating the minimum level of effectiveness one can expect when it is provided with label-only information. We show that questionnaire participants judge the GPT-4 graphs as the most accurate in the evaluated categories, closely followed by knowledge graphs constructed by domain experts, with causal Machine Learning (ML) far behind. We use these results to highlight the important limitation of causal ML, which often produces causal graphs that violate common sense, affecting trust in them. However, we show that pairing GPT-4 with causal ML overcomes this limitation, resulting in graphical structures learnt from real data that align more closely with those identified by domain experts, compared to structures learnt by causal ML alone. Overall, our findings suggest that despite GPT-4 not being explicitly designed to reason causally, it can still be a valuable tool for causal representation, as it improves the causal discovery process of causal ML algorithms that are designed to do just that. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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problexity — an open-source Python library for binary classification problem complexity assessment

arXiv

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arXiv 2022年

作者： Komorniczak, Joanna Ksieniewicz, Pawel Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

The classification problem’s complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning – becoming the basis for determining meta-attributes or multicriteria optimization – allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language – compatible with the scikit-learn programming interface – allowing for the implementation of research using them in the most popular programming environment of the machine learning community. © 2022, CC BY.

关键词： Python

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Ls-Stream: Lightening Stragglers in Join Operators for Skewed Data Stream Processing

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IEEE Transactions on computers 2025年

作者： Wu, Minghui Sun, Dawei Gao, Shang Li, Keqin Buyya, Rajkumar China University of Geosciences School of Information Engineering Beijing 100083 China Deakin University School of Information Technology Waurn Ponds 3216 VIC Australia State University of New York Department of Computer Science New Paltz 12561 NY United States University of Melbourne Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems Australia

Load imbalance can lead to the emergence of stragglers, i.e., join instances that significantly lag behind others in processing data streams. Currently, state-of-the-art solutions are capable of balancing the load between join instances to mitigate stragglers by managing hot keys and random partitioning. However, these solutions rely on either complicated routing strategies or resource-inefficient processing structures, making them susceptible to frequent changes in load between instances. Therefore, we present Ls-Stream, a data stream scheduler that aims to support dynamic workload assignment for join instances to lighten stragglers. This paper outlines our solution from the following aspects: (1) The models for partitioning, communication, matrix, and resource are developed, formalizing problems like imbalanced load between join instances and state migration costs. (2) Ls-Stream employs a two-level routing strategy for workload allocation by combining hash-based and key-based data partitioning, specifying the destination join instances for data tuples. (3) Ls-Stream also constructs a fine-grained model for minimizing the state migration cost. This allows us to make tradeoffs between data transfer overhead and migration benefits. (4) Experimental results demonstrate significant improvements made by Ls-Stream: reducing maximum system latency by 49.3% and increasing maximum throughput by more than 2x compared to existing state-of-the-art works. © 1968-2012 IEEE.

关键词： distributed stream computing Load balancing State migration Straggler instances Stream join

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Harnessing the Power of AI With Deep Learning: Innovations and Implications

Harnessing the Power of AI With Deep Learning: Innovations a...

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Computational, communication and Information Technology (ICCCIT), International Conference on

作者： Talluri Upender C. Sathiyamoorthy Udayakumar K D. Kirubakaran Purushothaman V Kiruba Thangam Raja Department of CSE CMR College of Engineering and Technology Medchal Hyderabad Telangana Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues.

关键词： Deep learning Technological innovation Ethics Recurrent neural networks Autonomous systems Finance Medical services Safety Internet of Things Artificial intelligence

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Artificial Intelligence driven security model for Internet of Medical Things (IoMT)

Artificial Intelligence driven security model for Internet o...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Cuddapah Anitha Komala C R Chettiyar Vani Vivekanand S D Lalitha Sampath Boopathi Revathi. R Department of Computer Science and Systems Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Information Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Electronics and Communication Engineering RMK College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Computer Science and Engineering R.M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Namakkal Tamil Nadu India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India

The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction.

关键词： Support vector machines COVID-19 Analytical models Computational modeling Internet of Medical Things Predictive models Security

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Novel Bendable Mnxca(0.90−X)Ni0.10fe2o4 Substrate Based Symmetrical Metamaterial Absorber for Rf Energy Harvesting Application to Achieve Sustainable Development Goals

SSRN

引用

SSRN 2024年

作者： Hasan, Mohammad Shakibul Islam, Mohammad Tariqul Ouda, Mohammad Alenezi, Abdulmajeed M. Soliman, Mohamed S. Samsuzzaman, Md. Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Electrical Engineering Technology College of Engineering & Technology University of Doha for Science and Technology Doha24449 Qatar Department of Electrical Engineering Faculty of Engineering Islamic University of Madinah Madinah41411 Saudi Arabia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer and Communication Engineering Faculty of Computer Science and Engineering Patuakhali Science and Technology University Bangladesh

This article presents a bendable substrate MnxCa(0.90−x)Ni0.10Fe2O4 based metamaterial absorber for RF energy harvesting from Wifi frequency of 5 GHz to the achievement of the Sustainable Development Goals. The bendable metamaterial absorber (BMMA) energy harvester unit cell constructed with the physical dimension of 21 × 21 *** proposed unit cell consist of cross sign shaped resonator surrounded by circular ring resonator is located middle portion and boundary split outer ring resonator. Based on the simulation findings, the proposed BMMA energy harvester provides maximum energy harvesting efficiency of 99.42% at 5 GHz. Due to the symmetrical design, the energy harvester unit cell shows excellent angle insensitive up to 90 degrees for incident angle and polarization angle. The proposed BMMA exhibits single negative properties, and it shows excellent harvesting efficiency for various bending conditions. The lightweight, flexibility of the structure indicate that MnxCa(0.90−x)Ni0.10Fe2O4 based bendable metamaterial absorber energy harvester have significant potential for RF energy harvesting application from wifi frequency of 5 GHz and it achieve Sustainable Development Goals (SDGs), specially alignment with SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities),and SDG 13 (Climate Action). © 2024, The Authors. All rights reserved.

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