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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

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Prediction of exosomal piRNAs based on deep learning for seq...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty

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arXiv 2022年

作者： Zhang, Yifei Ukil, Sourav Kumar Sperilă, Andrei Sabău, Şerban Department of Electrical and Computer Engineering Stevens Institute of Technology NJ07030 United States Department of Automatic Control and Computer Science "Politehnica" University of Bucharest Bucharest Romania

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Additives

A survey on secure decentralized optimization and learning

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arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers

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Sensor Fault Detection and Isolation in Autonomous Nonlinear...

European control Conference (ECC)

作者： John Cao M. Umar B. Niazi Matthieu Barreau Karl Henrik Johansson Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor measurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators.

关键词： Uncertainty Fault detection Noise Observers Numerical simulation Sensor systems Sensors

Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness

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arXiv 2024年

作者： Zhang, Kejia Weng, Juanjuan Cai, Yuanzheng Luo, Zhiming Li, Shaozi Department of Artificial Intelligence Xiamen University Xiamen361005 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Information Science and Technology Jinan University Guangzhou510632 China

Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision. While adversarial training (AT) has emerged as a promising defense strategy, our analysis reveals a critical limitation: AT-trained models exhibit a bias toward low-frequency features while neglecting high-frequency components. This bias is particularly concerning as each frequency component carries distinct and crucial information: low-frequency features encode fundamental structural patterns, while high-frequency features capture intricate details and textures. To address this limitation, we propose High-Frequency Feature Disentanglement and Recalibration (HFDR), a novel module that strategically separates and recalibrates frequency-specific features to capture latent semantic cues. We further introduce frequency attention regularization to harmonize feature extraction across the frequency spectrum and mitigate the inherent low-frequency bias of AT. Extensive experiments demonstrate our method’s superior performance against white-box attacks and transfer attacks, while exhibiting strong generalization capabilities across diverse scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Attack Impact Evaluation for Stochastic control Systems through Alarm Flag State Augmentation

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arXiv 2023年

作者： Sasahara, Hampei Tanaka, Takashi Sandberg, Henrik The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Department of Aerospace Engineering and Engineering Mechanics Cockrell School of Engineering The University of Texas AustinTX78712 United States The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This note addresses the problem of evaluating the impact of an attack on discrete-time nonlinear stochastic control systems. The problem is formulated as an optimal control problem with a joint chance constraint that forces the adversary to avoid detection throughout a given time period. Due to the joint constraint, the optimal control policy depends not only on the current state, but also on the entire history, leading to an explosion of the search space and making the problem generally intractable. However, we discover that the current state and whether an alarm has been triggered, or not, is sufficient for specifying the optimal decision at each time step. This information, which we refer to as the alarm flag, can be added to the state space to create an equivalent optimal control problem that can be solved with existing numerical approaches using a Markov policy. Additionally, we note that the formulation results in a policy that does not avoid detection once an alarm has been triggered. We extend the formulation to handle multi-alarm avoidance policies for more reasonable attack impact evaluations, and show that the idea of augmenting the state space with an alarm flag is valid in this extended formulation as well. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation

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SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)

作者： Jiusi Zhang Jilun Tian Yuchen Jiang Hao Luo Cong-Sheng Huang Mo-Yuen Chow Shen Yin Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A. Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches.

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GBMIA: Gradient-based Membership Inference Attack in Federated Learning

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GBMIA: Gradient-based Membership Inference Attack in Federat...

IEEE International Conference on Communications (ICC)

作者： Xiaodong Wang Naiyu Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Mathematics & Computer Science Fayetteville State University Fayetteville NC USA Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.

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