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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems System performance Process control Watermarking Control systems Temperature control Signal design

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地理大数据揭示中国主要城市建成环境物质存量的空间格局

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engineering 2024年第3期34卷 143-153页

作者： Zhou Huang Yi Bao Ruichang Mao Han Wang Ganmin Yin Lin Wan Houji Qi Qiaoxuan Li Hongzhao Tang Qiance Liu Linna Li Bailang Yu Qinghua Guo Yu Liu Huadong Guo Gang Liu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space SciencesPeking UniversityBeijing 100871China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China SDU Life Cycle Engineering Department of Green TechnologyUniversity of Southern DenmarkOdense 5230Denmark Department of Construction Management School of Civil EngineeringTsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China School of Geographical Sciences East China Normal UniversityShanghai 200062China Land Satellite Remote Sensing Application Center Ministry of Natural ResourcesBeijing 100048China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Geography California State UniversityLong BeachCA 90840USA Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China College of Urban and Environmental Sciences Peking UniversityBeijing 100871China

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate ***,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution *** study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained *** per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form *** is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past ***’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in *** results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

关键词： Urban system engineering Built environment stock Spatial pattern Urban sustainability Big geodata

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Comparing SWIPT Techniques for Zero-Energy RIS

Comparing SWIPT Techniques for Zero-Energy RIS

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IEEE International Conference on Communications Workshops, ICC

作者： Dimitrios Tyrovolas Vasilis K. Papanikolaou Sotiris A. Tegos Yue Xiao Panagiotis D. Diamantoulakis Sotiris Ioannidis Christos Liaskos George K. Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Chania Chania Greece School of Information Science and Technology Southwest Jiaotong University Chengdu China Computer Science Engineering Department University of Ioannina Ioannina and Foundation for Research and Technology Hellas (FORTH) Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Providing enhanced quality of service (QoS) to support demanding applications in the most energy-efficient way has been identified as a primary objective of future 6G networks. Towards this direction, the concept of programmable wireless environments (PWEs) has emerged, wherein extremely high QoS can be ensured by controlling the wireless propagation through reconfigurable intelligent surfaces (RIS). To meet the demands of 6G networks in terms of energy efficiency, in this paper, zero-energy RISs (ZERISs) are introduced, which harvest the required amount of energy for their operation and then contribute to the PWE conversion. More specifically, we investigate the performance of a ZERIS-assisted communication system in terms of joint energy-data rate outage probability, which quantifies the trade-off between the harvested energy and information transmission, for three distinct simultaneous wireless information and power transfer (SWIPT) methods are investigated, namely power splitting, time switching, and element splitting. Finally, simulation results validate the derived analysis, leading to useful insights for the ZERIS-assisted network regarding which energy harvesting method achieves better performance in terms of joint energy-data rate outage probability.

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A 3M-Hybrid Model for the Restoration of Unique Giant Murals: A Case Study on the Murals of Yongle Palace

arXiv

引用

arXiv 2023年

作者： Yang, Jing Raihana Ruhaiyem, Nur Intan Zhou, Chichun School of Computer Sciences Universiti Sains Malaysia Penang Gelugor11800 Malaysia Shanxi Datong University Shanxi Datong037000 China School of engineering Dali university Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center of Yunnan Provincial Department of Education Yunnan671003 China

The Yongle Palace murals, as valuable cultural heritage, have suffered varying degrees of damage, making their restoration of significant importance. However, the giant size and unique data of Yongle Palace murals present challenges for existing deep-learning based restoration methods: 1) The distinctive style introduces domain bias in traditional transfer learning-based restoration methods, while the scarcity of mural data further limits the applicability of these methods. 2) Additionally, the giant size of these murals results in a wider range of defect types and sizes, necessitating models with greater adaptability. Consequently, there is a lack of focus on deep learning-based restoration methods for the unique giant murals of Yongle Palace. Here, a 3M-Hybrid model is proposed to address these challenges. Firstly, based on the characteristic that the mural data frequency is prominent in the distribution of low and high frequency features, high and low frequency features are separately abstracted for complementary learning. Furthermore, we integrate a pre-trained Vision Transformer model (VIT) into the CNN module, allowing us to leverage the benefits of a large model while mitigating domain bias. Secondly, we mitigate seam and structural distortion issues resulting from the restoration of large defects by employing a multi-scale and multi-perspective strategy, including data segmentation and fusion. Experimental results demonstrate the efficacy of our proposed model. In regular-sized mural restoration, it improves SSIM and PSNR by 14.61% and 4.73%, respectively, compared to the best model among four representative CNN models. Additionally, it achieves favorable results in the final restoration of giant murals. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Haozhao Wang Wenchao Xu Yunfeng Fan Ruixuan Li Pan Zhou School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods.

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Provable Sim-to-real Transfer in Continuous Domain with Partial Observations

arXiv

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arXiv 2022年

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is often cheaper, safer and much faster to collect samples in simulation than in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity

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NeuroImage 2024年 304卷 120956-120956页

作者： Namgung, Jong Young Mun, Jongmin Park, Yeongjun Kim, Jaeoh Park, Bo-yong Department of Data Science Inha University Incheon South Korea Data Sciences and Operations Department Marshall School of Business University of Southern California Los Angeles United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Department of Brain and Cognitive Engineering Korea University Seoul South Korea Center for Neuroscience Imaging Research Institute for Basic Science Suwon South Korea

Autism spectrum disorder (ASD) is an atypical neurodevelopmental condition with a diagnostic ratio largely differing between male and female participants. Due to the sex imbalance in participants with ASD, we lack an understanding of the differences in connectome organization of the brain between male and female participants with ASD. In this study, we matched the sex ratio using a Gaussian mixture model-based oversampling technique and investigated the differences in functional connectivity between male and female participants with ASD using low-dimensional principal gradients. Between-group comparisons of the gradient values revealed significant interaction effects of sex in the sensorimotor, attention, and default mode networks. The sex-related differences in the gradients were highly associated with higher-order cognitive control processes. Transcriptomic association analysis provided potential biological underpinnings, specifying gene enrichment in the cortex, thalamus, and striatum during development. Finally, the principal gradients were differentially associated with symptom severity of ASD between sexes, highlighting significant effects in female participants with ASD. Our work proposed an oversampling method to mitigate sex imbalance in ASD and observed significant sex-related differences in functional connectome organization. The findings may advance our knowledge about the sex heterogeneity in large-scale brain networks in ASD. © 2024 The Author(s)

关键词： Autism spectrum disorder Class imbalance Functional gradient Gaussian mixture model Oversampling Sex difference

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

引用

arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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