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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability. © 2022 IEEE.

关键词： Aggregates

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Performance Realization for Gas Turbine system: Adaptive Constraint Regulation Framework

Performance Realization for Gas Turbine System: Adaptive Con...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Peihao Du Weimin Zhong Hongyi Li Linlin Li Xin Peng Kaixun He Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Electronic and Information Engineering and Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University Chongqing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

With the excessive consumption of fossil energy and the growing environmental pollution caused by its combustion, the gas turbine system (GTS) plays a key role in distributed renewable energy fields due to its versatility, efficiency and controllability. This paper focuses on the adaptive constraint regulation problem for the GTS with time-varying load disturbance, thereby facilitating exact speed tracking to regulate the fuel flow for the turbine under variable load conditions. Therein, a residual transformation dynamics based on the performance function are established for triggering the preset-time residual convergence and output performance constraints. Then, an adaptive constraint controller is developed to ensure the desired regulation level of the GTS. Simulation results illustrate that the proposed strategy can mitigate the load disturbance effect and guarantee the GTS performance constraints.

关键词： Renewable energy sources Pollution Simulation Reliability engineering Regulation Stability analysis Turbines Time-varying systems Convergence Load modeling

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Defect Detection of Rubber Gloves Based on Normal Samples 6

Defect Detection of Rubber Gloves Based on Normal Samples

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6th IEEE International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2021

作者： Yu, Naigong Wang, Honglu Xu, Qiao Lin, Jia Faculty of Information Technology Ministry of Education Beijing University of Technology Beijing Key Laboratory of Computing Intelligence and Intelligent System Engineering Research Center of Digital Community Beijing100124 China

ISBN: (纸本)9780738105338

Rubber gloves are widely favored in various industries, and the defect detection link in production is very important. This paper proposes a method for detecting defects in rubber gloves based on normal samples (no defects). First, random noise is added to the collected rubber glove images, and the size is standardized and unified. Secondly, establish an improved GANomaly network model. According to the small and difficult defect to distinguish characteristics of black spots and oil defects of rubber gloves, a noise-reducing convolutional autoencoder is built in the network model as a generation network, and the least square loss is introduced for the model of confrontation training. The model only uses normal sample input for training, learning the characteristic distribution of the normal sample and performs image reconstruction, and judges whether it has defects according to the reconstruction effect score of the input sample during testing. The experiment is based on the rubber glove image data set, and the results show that the method can accurately identify whether there are defects on the rubber glove. © 2021 IEEE.

关键词： Defects

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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A Federated Learning Network Intrusion Detection system for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection system (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning Network intrusion detection Signal processing Benchmark testing Speech processing

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Joint Optimization Algorithm for Adaptive Bit Allocation Based on Temporal-Spatial Information

Joint Optimization Algorithm for Adaptive Bit Allocation Bas...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Shaokang Wang Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) China Ministry of Education Engineering Research Center of Blockchain and Network Convergence Technology China

Versatile Video Coding (VVC) standard further improves the video compression ratio. Despite inter prediction and intra prediction effectively eliminating temporal and spatial residual information, there is still room for further improvement in rate control (RC) accuracy and rate distortion (R-D) performance. In view of the above issues, the joint optimization RC algorithm for adaptive bit allocation for VVC is proposed. In this paper, the proposed algorithm utilizes a specific pre-analysis mechanism so that the Temporal-Spatial Information (TSI) is obtained. And then the TSI is integrated into the RC model so as to optimize adaptive bit allocations for frame-level and coding tree unit level (CTU-level). Experimental results have shown that compared to the default RC under common test conditions (CTC), the proposed RC algorithm achieves obvious 2.26% Bjøntegaard Delta rate (BD-rate) saving, and the RC accuracy can be improved in the Random Access (RA) configuration.

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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A fully-customized dataflow engine for 3D earthquake simulation with a complex topography

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Science China(Information Sciences) 2022年第5期65卷 65-80页

作者： Bingwei CHEN Haohuan FU Wayne LUK Guangwen YANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University National Supercomputer Center in Wuxi Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Computing Imperial College London

With HPC(high performance computing) evolving into the exascale era, improvements in computing performance and power efficiency have become increasingly more important. Based on our previous work on enabling earthquake simulations on a large scale on Sunway Taihu Light, we further explore other possibilities to improve the application through a fully-customized hardware design on reconfigurable FPGA(field programmable gate array) devices. We investigate the feasibility and the potential benefits of a complete fixed-point design. We first perform a coarse-resolution-based simulation to analyze the representation range and precision needed to capture both the total energy and the energy distribution of variables over space and time. We then derive a complete fixed-point design that identifies the suitable bitwidth for major categories of variables and dynamically represents the range through a dynamic scaling scheme. Finally, we use the optimized fixed-point design to run a case of the Wenchuan earthquake to demonstrate the potential of supporting large-scale scientific simulations on FPGA devices. The results demonstrate that an 18-bit fixed-point design already provides an almost identical description of the seismic events in the Wenchuan scenario down to a single-precision floating-point version and provides sustainable performance equivalent to 13.1 Intel Xeon Gold 6154 18-core CPUs or 2.10 Sunway 260-core processors, with performance per watt(power efficiency) improved by 15.3 and 3.72 times compared with the Intel Xeon Gold 6154 18-core CPUs and the Sunway 260-core processors, respectively.

关键词： reconfigurable computing earthquake simulation FPGA hardware-software co-design architecture exploration

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TGCN-DA: A Temporal Graph Convolutional Network with Data Augmentation for High Accuracy Insider Threat Detection

TGCN-DA: A Temporal Graph Convolutional Network with Data Au...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Ximing Li Linghui Li Xiaoyong Li Binsi Cai Bingyu Li Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Insider threats present a formidable challenge to cybersecurity, as insiders possess the privileges and information necessary to execute diverse attacks. A comprehensive analysis of user behavior, including behavioral features, sequences, and inter-user relationships, is required for effective insider threat detection. However, few existing methods consider these features in an integrated manner, which could result in high false positives. To further improve the accuracy of insider threat detection, we propose a novel framework for insider threat detection based on a temporal graph convolutional network with data augmentation (referred to as TGCN-DA), which integrates the exploration of structural information among users and simultaneously captures the behavior temporal dependencies. In particular, we introduce an edge predictor to encode user structural information and strengthen intra-class edges among users based on the representation of users’ behavior. Additionally, the GCN with temporal feature mechanism is leveraged to learn dynamic changes in users’ behavior to capture behavior temporal dependence. Extensive experiments demonstrate that our proposed TGCN-DA outperforms other state-of-the-art methods and achieves higher accuracy in the task of insider threat detection.

关键词： Data privacy Image edge detection Neural networks Data augmentation Feature extraction Threat assessment Task analysis

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Characterizations of the biomineralization film caused by marine Pseudomonas stutzeri and its mechanistic effects on X80 pipeline steel corrosion

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Journal of Materials Science & technology 2022年第30期125卷 15-28页

作者： Haixian Liu Wen Chen Yu Tan Guozhe Meng Hongfang Liu YFrank Cheng Hongwei Liu School of Chemical Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China Research Institute of Natural Gas Technology Southwest Oil&Gasfield CompanyChina National Petroleum CorporationChengdu 610000China Department of Mechanical and Manufacturing Engineering University of CalgaryCalgaryAlberta T2N 1N4Canada Key Laboratory for Large-Format Battery Materials and System Ministry of EducationHubei Key Laboratory of Materials Chemistry and Service FailureSchool of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Microbiologically influenced corrosion(MIC)of steel generates a corrosion product film,which can also be called biomineralization *** is critical to understand the structure of biomineralization film since it dominates the corrosion behavior of *** this work,Pseudomonas stutzeri(***)was isolated from seawater,and the biomineralization film caused by marine *** was characterized by Transmission electron microscopy(TEM),scanning electron microscopy(SEM),X-ray diffraction(XRD),*** mechanistic effects of the biomineralization film on X80 pipeline steel corrosion were also *** results indicate that the minerals are mainly composed of nano FeOand FeOOH,according to TEM and XRD *** particle sizes of biominerals are below 10 *** work also provides an insight strategy to prepare nanomaterials by MIC caused by *** addition,*** can grow well with COas a carbon source and iron as an electron *** corrosion rates(CRs)of specimens are closely related to the structure of biomineralization *** CRs increase with the decrease of initial cell *** with an initial concentration of 10~7 cells/mL can promote the formation of a compact biomineralization film with a thickness of 145.8±4.8μm,leading to corrosion inhibition with a CR of 0.058±0.008 mm/*** some corrosion pits can be observed due to the formation of small *** impedance spectroscopy(EIS)data show higher impedance values and two time-constants,which imply the formation of a compact biomineralization film.

关键词： Biomineralization Nanomaterials MIC Pseudomonas stutzeri Steel

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