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Chinese Control Conference (CCC)

作者： Jian Wang Yanheng Liu Tianzhe Dong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun P. R. China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun P. R. China

Through analyzing the security problems which exist in general on-line payment systems, this paper proposes a scheme based on WPKI (Wireless Public key Infrastructure) and further designs and implements a gas station on-line payment system. By using WPKI technology which includes SSL (Secure Sockets Layer) protocol, digital signature and digital certificate, the designed on-line payment system can achieve data privacy, authenticity, integrity and non-repudiation during on-line payment process.

关键词： Servers Protocols Authentication Business Encryption Public key

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data Stream Classification Based on Extreme Learning Machine: A Review

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Big data Research 2022年 30卷

作者： Zheng, Xiulin Li, Peipei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing 100084 China

Many daily applications are generating massive amount of data in the form of stream at an ever higher speed, such as medical data, clicking stream, internet record and banking transaction, etc. In contrast to the traditional static data, data streams are of some inherent properties, to name a few, infinite length, concept drift, multiple labels and concept evolution. Among all the data mining tasks, classification is one of the basic topics in data stream mining and has gained more and more attentions among different research communities. Extreme Learning Machine (ELM) has drawn much interests in data classification due to its high efficiency, universal approximation capability, generalization ability, and simplicity, which have greatly inspired the development of many ELM-based algorithms and their applications during the past decades. In this paper, we mainly provide a comprehensive review on ELM theoretical research and its variants in data stream classification, and categorize these algorithms from different perspectives. Firstly, we briefly introduce the basic principles of ELM and its characteristics. Secondly, we give an overview of different ELM variants to address the particular issues of data stream classification. Thirdly, we present an overview of different strategies to optimize the ELM, which have further improved the stability, accuracy and generalization ability of ELM, and briefly introduce some practical applications of ELM in data stream classification. Finally, we conduct several groups of experiments to compare the performance of ELM based models addressing the focused issues. Also, the open issues and prospects of ELM models used for stream classification are discussed, which are worthwhile to be further studied in the future. © 2022

关键词： Classification data stream Extreme learning machine

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Fast One-Stage Unsupervised Domain Adaptive Person Search

arXiv

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arXiv 2024年

作者： Cui, Tianxiang Wang, Huibing Peng, Jinjia Deng, Ruoxi Fu, Xianping Wang, Yang The College of Information and Science Technology Dalian Maritime University China The College School of Cyber Security and Computer Hebei University China The College of Computer Science and Artificial Intelligence Wenzhou University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China

Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increases model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementary integrates domain adaptaion with label adaptaion within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at https://***/whbdmu/FOUS © 2024, CC0.

关键词： Iterative methods

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Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-16页

作者： Chu, Liang Hou, Zhuoran Jiang, Jingjing Yang, Jun Zhang, Yuanjian State Key Laboratory of Automotive Simulation and Control Jilin University No. 5988 Renmin Street Nanguan District Changchun China Department of Aeronautical and Automotive Engineering Loughborough University Leicestershire UK

Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model. IEEE

关键词： Feature extraction

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Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions

arXiv

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arXiv 2019年

作者： Niu, Yulei Zhang, Hanwang Lu, Zhiwu Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering Columbia University New YorkNY10027 United States

—We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., "largest elephant standing behind baby elephant". This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context — visual attributes (e.g., "largest", "baby") and relationships (e.g., "behind") that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Specifically, our framework exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. In addition to reciprocity, our framework considers the semantic information of context, i.e., the referring expression can be reproduced based on the estimated context. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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Fault detection for rolling bearings by multi-sensor information fusion method with adaptive weights

Fault detection for rolling bearings by multi-sensor informa...

引用

data Driven Control and Learning Systems (DDCLS)

作者： Hao Wu YingHao Zhao Xu Yang Jian Huang Jiarui Cui 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

Driven by the increasing needs for production safety, a fault detection method based on multi-sensor fusion with adaptive weight coefficients is proposed in this paper to make full use of multi-measuring points information. To this end, considering the different information among multi-measuring points, the variance contribution rate (VCR) of vibration signals are used to design adaptive weight coefficients for data fusion to fully utilize the information contained in each vibration signal. On this basis, the least atoms contain time domain and frequency domain are extracted based on dictionary sparse representation (DSR) algorithm to represent the feature information of the original signal to weaken the influence of the curse of dimensionality. Finally, K-nearest neighbor distance is used in sparse residual space (SRS) for fault detection (K-SRS). The effectiveness of the proposed method is demonstrated by the rolling bearings data, and results show the advantage of our proposed approach.

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Enhancing Information Extraction from Low-sample Materials Science Literature by Transfer Learning

Enhancing Information Extraction from Low-sample Materials S...

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International Conference on Big data and Information Analytics (BigDIA)

作者： Mengzhe Hei Qingbao Liu Xin Zhang National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Extracting information from materials science liter-ature is an important task that can provide data support for data-driven material design methods. This work is not straight-forward, especially in low-sample material domains where the data volume is insufficient to support the accurate training of extraction models. To tackle this problem, we proposes the use of a pre-trained model and a feature-based transfer learning approach to transfer the extraction capabilities learned from large-sample material domains to low-sample domains. To enhance the performance of information extraction, we propose a extraction method based on MatSciBERT and pointer networks. To evaluate the performance of the model, we have annotated datasets across three domains and conducted extensive experiments. The experiments demonstrate that our transfer method can improve extraction models in low-sample domains.

关键词： Training Materials science and technology Solid modeling Neuromorphics Transfer learning Metals Feature extraction Information retrieval data models data mining

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Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

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Journal of Geographical Sciences 2019年第9期29卷 1441-1461页

作者： Gao Jianbo FANG Peng YUAN Lihua State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Institute of Complexity Science and Big Data Technology Guangxi UniversityNanning 530004China Wuhan National Laboratory for Optoelectronics Key Laboratory of In formation Storage SystemEngineering Research Center of Data Storage Systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Center for GeoData and Analysis Faculty of Geographical ScieneeBeijing Normal UniversityBeijing 100875China

Since 2005,dozens of geographical observational stations have been established in the Heihe River Basin(HRB),and by now a large amount of meteorological,hydrological,and ecological observations as well as data pertaining to water resources,soil and vegetation have been *** adequately analyze these available data and data to be further collected in future,we present a perspective from complexity *** concrete materials covered include a presentation of adaptive multiscale filter,which can readily determine arbitrary trends,maximally reduce noise,and reliably perform fractal and multifractal analysis,and a presentation of scale-dependent Lyapunov exponent(SDLE),which can reliably distinguish deterministic chaos from random processes,determine the error doubling time for prediction,and obtain the defining parameters of the process *** adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data,and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal *** SDLE is illustrated to determine intermittent chaos from river flow data.

关键词： Heihe River basin geographical observation complexity theory adaptive multiscale filter fractal analysis scale-dependent Lyapunov exponent

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IRMD: Malware Variant Detection Using Opcode Image Recognition

IRMD: Malware Variant Detection Using Opcode Image Recogniti...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jixin Zhang Zheng Qin Hui Yin Lu Ou Yupeng Hu College of Computer Science and Electronic Engineering Hunan University Hunan China Hunan Key Laboratory of Big Data Research and Application Hunan China

ISBN: (纸本)9781509053827

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By using convolutional neural network to compare the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables is malicious. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15% of detection accuracy when the detection set contains a large quantity of binaries and the training set is much smaller.

关键词： Malware Image recognition Training Feature extraction Image reconstruction Histograms Neural networks

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Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging based on Adversarial Learning

Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging...

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IEEE International Conference on Image Processing

作者： Xiaoteng Chen Junying Chen School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education China

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

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