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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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A Two-Stage Enhancement Method for Object Detection on Low-Resolution Images

A Two-Stage Enhancement Method for Object Detection on Low-R...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zijian Yuan Mingyang Ling Kan Chang Mengyu Yin Minghong Li Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Automation Central South University Changsha China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Detecting objects on low-resolution (LR) images is a challenging task, as small structures and details contained in LR images are insufficient. To improve the detection accuracy on LR images, a two-stage enhancement framework is proposed in this paper. In the first stage, a lightweight image super-resolution (SR) sub-network is built by incorporating the external guidance obtained by the detection backbone, so that the potential discriminant features can be well preserved. Moreover, to alleviate the hardship of directly learning a nonlinear mapping from the LR space to the high-resolution (HR) space, an auxiliary module which maps the SR results back to the degraded LR images provides additional constraint. In the second stage, the multi-scale features produced by the detection heads are further enhanced by the lightweight feature adaptation modules, which aim to further bridge the gap between the SR result and the corresponding HR image. During training, the detector is kept frozen in our framework, leading to a flexible plug-and-play mechanism. Experiments demonstrate that our approach achieves better performance on LR images than many state-of-the-art methods. To facilitate further study, our source code will be available at: https://***/Zijian-Yuan/TSE-YOLO.

关键词： Training Upper bound Head Accuracy Source coding Superresolution Detectors

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An Integrated AI-Based Information System for Energy Forecasting

An Integrated AI-Based Information System for Energy Forecas...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Panagiotis Skaloumpakas Zoi Mylona Sofoklis Strompolas Elissaios Sarmas Haris Doukas HOLISTIC IKE Athens Greece Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

The aim of reducing carbon emissions through the digitization of the energy sector involves several challenges. One potential solution is the integration of renewable energy sources; however, the volatile and intermittent nature of this type of energy requires precise forecasting. This paper presents a novel, intelligent information system that covers the entire data lifecycle in the energy sector, including data acquisition, processing and storage, model development and training, and a user-friendly web application. The system was tested in three real-life use cases and demonstrated effectiveness and versatility in addressing the intricate data challenges faced by the energy industry. The paper concludes with suggestions for further research in this area.

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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Intermountain engineering, Technology and computing (IETC)

作者： Nathaniel Sweeney Caroline Xu Joseph A. Shaw Toby D. Hocking Bradley M. Whitaker Electrical and Computer Engineering Department Montana State University USA Electrical Engineering and Computer Science Department University of Michigan USA Optical Technology Center Montana State University USA School of Informatics Computing and Cyber Systems Northern Arizona University USA

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training.

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What limits limits?

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National Science Review 2021年第1期8卷 11-14页

作者： Yong-Chun Liu Kun Huang Yun-Feng Xiao Lan Yang Cheng-Wei Qiu State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Frontier Science Center for Quantum Information Tsinghua University Department of Optics and Optical Engineering University of Science and Technology of China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Department of Electrical and Systems Engineering Washington University Department of Electrical and Computer Engineering National University of Singapore

There are fundamental rules and principles setting the limits of physical systems. It triggers an interesting thought—can we break the limits under specific circumstances? A realistic system can only provide limited functionalities because its performance is physically constrained by some fundamental principles.‘Breaking the limit’, which usually implies that the capability of a system could be enhanced significantly,

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A web-based Building Automation and Control Service 13

A web-based Building Automation and Control Service

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Sarmas, Elissaios Dimitropoulos, Nikos Strompolas, Sofoklis Mylona, Zoi Marinakis, Vangelis Giannadakis, Athanasios Romaios, Alexandros Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece Holistic Ike Athens Greece University of Peloponnese Mechanical Engineering Department Patra Greece Aeronautics University of Patras Department of Mechanical Engineering Patra Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The reduction of the environmental impact in the building sector is necessary in achieving global sustainability. In this context, Building Automation and Control systems provide the opportunity for efficient monitoring and control facilities' subsystems, such as the heating and cooling system, the ventilation system, the hot water system, the lighting appliances among others, with the goal of improving thermal comfort as well as energy efficiency. This paper presents a Building Automation and Control system aiming at facilitating data-driven monitoring of complex, multi-storey facilities, by disagreggating total consumption of the different floors and rooms of the building and offering advanced insights and benchmarking indicators. The service is showcased with a use case application on a real building, where the benefits of the service for the energy manager are highlighted. © 2022 IEEE.

关键词： Energy efficiency

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Energy Sector Digitilisation: A Security Framework Application for Role-Based Access Management

Energy Sector Digitilisation: A Security Framework Applicati...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Georgios Kormpakis Panagiotis Kapsalis Konstantinos Alexakis Zoi Mylona Sotiris Pelekis Vangelis Marinakis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece HOLISTIC Athens SA Greece

Data security and privacy in the energy sector and especially in buildings' energy management, is an emerging issue which cannot be underestimated, thus, calls for solid and robust solutions. The reliable functioning of energy management data platforms is essential for the efficient operation and performance of buildings, as well as the well-being of occupants. The fast-paced digital advances have aided this direction, but they have also created increased challenges for operation disruption mitigation and immutable services and outcomes. Data obtained from energy management systems frequently comprises highly sensitive and personal information. Consequently, access to such data, whether for reading purposes or for conducting experiments and research, must be strictly restricted and clearly defined. Through security frameworks data protection and information privacy, mitigation of risks and vulnerabilities can be ensured. In this paper, an end-to-end Security Framework is proposed for securing sensitive resources in energy management systems using Keycloak, OAuth2 and UMA 2 protocols as well as an extra security layer on top of the Keycloak, so as to enhance the overall security and avoid immediate interaction with Keycloak, which is the main user storage and access management component. The main concept of this framework is the controlled access of sensitive data and services, such as datasets, databases, functionalities, URLs, APIs, etc., leveraging the OAuth2 access tokens capabilities and the roles of the users. Our proposed architecture ensures that all the functionalities of the involved tools and protocols are properly secured end-to-end. The several existing functionalities of a number of tools and related protocols are properly leveraged and further extended, resulting to a fine-grained architecture with an extra security layer that offers administrative, security and identity access management and orchestration.

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Sequential fusion estimation for multisensor systems with non-Gaussian noises

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Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

关键词： state estimation sequential fusion non-Gaussian disturbance heavy-tailed noise multivariate t-distribution

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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Model authentication hashing: Identifying pirated neural network models

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Expert systems with Applications 2025年 283卷

作者： Xiong, Cheng Feng, Guorui Sun, Yunlong Qian, Zhenxing Yao, Heng Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Department of Tourism Fudan university Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of China Ministry of Culture and Tourism Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

Nowadays, a great number of neural network models have been trained and applied in many fields. At the same time, the emergence of vast network models has already highlighted the security problems of intellectual property protection. High-value pre-trained models of original inventors may be pirated and used by illegal users without authorization. In order to solve this problem, we first give the definition of model differentiation, and then propose an effective robust hashing method based on model parameters for piracy identification in this paper. Through our model hash generation network, the parameters of original model are transformed into a one-way hash sequence with the fixed length, and the robustness and discrimination of the model hashes can be utilized to authenticate model parameters. Experimental results show that the accuracy rates of similar model detection and different model discrimination under different cases are almost all greater than 96.5%, and the comprehensive performance of proposed method is also the best one compared with other 3 SOTA methods. These results mean that the proposed method can be applied for identifying illegal model piracy effectively. © 2025 Elsevier Ltd

关键词： Terrorism

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