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作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

It is not foolproof for intrusion detection to focus only on the network level and the program level. Internal security and external security of information systems should be given equal attention. User-level intrusion detection can deter and curtail attackers from damaging information systems. Even if the mimic attacker has gained and enhanced the host user privileges that he illegally obtained. In this paper, a novel method based on recurrent neural networks (RNNs) is used to predict user command sequences and prophesy user behaviors. The experimental results show that our command sequence-to-sequence model is robust and effective for solving long sequential problem on three different data sets including Purdue University data set, SEA data set and self-collected data set. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Recurrent neural networks

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Adaptive multiple-view label propagation for semi-supervised classification 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Jia, Lei Zhang, Huan Zhang, Zhao School of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Computer and Information Science Hefei University of Technology Hefei China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

We propose a novel Adaptive Multiple-view Label Propagation (MLP) framework for semi-supervised classification. MLP performs classification on multiple views rather than on the single view, and can exploit the complementarity of multiple views in the label prediction process. Moreover, MLP integrates the multi-view label propagation and the adaptive multiple graph weight learning into a unified model, where a linear transformation is used to enforce different weights form different view spaces. Thus, an optimal graph weight matrix can be constructed from each view. Due to the adaptive manner for defining the weight matrices from multiple views, MLP can avoid the complex and tricky process to select the neighborhood size or kernel parameter. Extensive results on real data show that MLP can deliver the enhanced performances, by comparing with other related techniques. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Linear transformations

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Ligand-rich oxygen evolution electrocatalysts reconstructed from metal-organic frameworks for anion-exchange membrane water electrolysis

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Science Bulletin 2025年第12期70卷 1976-1985页

作者： Li, Shiqi Liu, Weiwei Shi, Yi Wang, Tang Liu, Tianyang Xue, Xiang Li, Rui Qiao, Man Wu, Zhen-Yu Zhang, Wang College of Materials Science and Engineering College of Environment Zhejiang University of Technology Hangzhou310014 China Department of Chemistry Institute of Innovative Material Guangdong Provincial Key Laboratory of Sustainable Biomimetic Materials and Green Energy Southern University of Science and Technology Shenzhen518055 China Jiangsu Co-Innovation Centre of Efficient Processing and Utilization of Forest Resources College of Chemical Engineering Nanjing Forestry University Nanjing210037 China Institute of Advanced Materials and Flexible Electronics School of Chemistry and Materials Science Nanjing University of Information Science & Technology Nanjing210044 China

Organic ligands in metal-organic frameworks (MOFs) play an indispensable role in the reconstruction and catalysis during the alkaline oxygen evolution reaction (OER). However, it is still a big challenge to maintain a high content of ligands in MOF-reconstructed OER electrocatalysts and to study the interaction between ligands and derived (oxy)hydroxides. Herein, a ligand-rich trimetallic amorphous electrocatalyst is fabricated through a two-step mechanochemical and electrochemical reconstruction strategy. Experimental and theoretical studies clearly reveal that the d-π interaction between delocalized π-electrons on the benzene ring of ligands and derived (oxy)hydroxides, can trigger the charge transfer from ligands to the active metal centers, thus optimizing the adsorption energy of the oxygen-containing intermediates and enhancing the OER performance. Moreover, an anion-exchange membrane water electrolyzer using such ligand-rich OER electrocatalyst can be operated steadily at 1.69 V and 55 °C under an industrial-level current density of 500 mA cm−2 for over 200 h. This work provides novel insights into the role of organic ligands in alkaline OER electrocatalysis, with the potential to facilitate the production of green hydrogen at industrial-level current densities. © 2025 Science China Press

关键词： Electrolysis

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

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Pattern-Dependent Equalization for PAM-4 IM/DD System with Severe Bandwidth Limitation

Pattern-Dependent Equalization for PAM-4 IM/DD System with S...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Wei Wang Dongdong Zou Weihao Ni Zhaohui Li Fan Li Yi Cai Qi Sui School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou China Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network Technology Soochow University Suzhou China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Technology Zhuhai China

LUT enabled pattern-dependent equalization combined with MLSE is proposed for bandwidth-limited IM/DD system. The experimental results show that the proposed method can provide significant improvement compared to the ... 详细信息

关键词： Maximum likelihood estimation Sensitivity C-band Asia Bandwidth Receivers Table lookup

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of computer Science & technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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An ontology-supported inquiry learning technique

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous information. All rights reserved.

关键词： Curricula

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

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Journal on Big Data 2021年第3期3卷 97-110页

作者： Wei Fang Yupeng Chen Qiongying Xue School of Computer&Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou215006China

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep *** algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,*** of its excellent performance in processing Spatio-temporal sequence *** them,algorithms based on LSTM and GRU have developed most rapidly because of their good *** paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting *** the same time,it also compares the advantages and disadvantages,and innovations of each *** purpose of this article is to give readers a clear understanding of solutions to such ***,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

关键词： RNN LSTM GRU spatio-temporal sequence prediction

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Decision-Based Iterative Fragile Watermarking for Model Integrity Verification

SSRN

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SSRN 2023年

作者： Yin, Zhaoxia Yin, Heng Su, Hang Zhang, Xinpeng Gao, Zhenzhe Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai Shanghai200241 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Anhui Hefei230000 China Department of Computer Science and Technology Tsinghua University Beijing Beijing100000 China School of Computer Science and Technology Fudan University Shanghai Shanghai200241 China School of Communication and Electronic Engineering East China Normal University Shanghai Shanghai200241 China

Foundation models are commonly hosted on cloud servers to cater to the high demand for their services. However, this exposes them to security risks, as attackers can manipulate these models by introducing backdoor patterns after uploading them to the cloud or during transfer from a local *** mitigate this issue, we propose an iterative decision-based fragile watermarking algorithm to verify the integrity of remote models. This algorithm transforms normal training samples into fragile samples that exhibit sensitivity to model changes. During validation, we compare the output of sensitive samples from the original model to that of the compromised model, enabling us to assess the completeness of the *** method offers several advantages: Firstly, the iterative update of samples occurs in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model. This approach reduces the risk of exposure to adversarial attacks. Secondly, even with changes in the overall parameters of the model at a magnitude of 1e-4, the fragile samples can detect such changes. Lastly, our method is independent of the specific *** experiments demonstrate the effectiveness of our proposed method on various models and datasets, highlighting its superior performance compared to the current state-of-the-art techniques. © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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