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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Liu, Peng Yu, Zhiyong Huang, Fangwan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

To achieve progressive and accurate decision-making for long-term time series data while meeting the needs of privacy-friendly and early, this paper proposes a universal framework for sequential progressive decision-making (SPD). This framework first segments the data and sets up multiple columns of neural networks according to the number of segments. Each column can make segmented decisions based on the inputs for the period. Additionally, without sharing the original data, the framework leverages lateral hidden layer connections between preceding and succeeding columns to obtain useful features for subsequent decision-making, gradually improving accuracy while avoiding the risk of data leakage. SPD has the advantages of privacy friendliness, column model diversification, prior knowledge reuse, and easy scalability, making it an effective framework for continuous decision-making. The effectiveness of that was validated using various network models in handwritten digit recognition and electrocardiogram classification tasks. The obtained experimental results reveal that SPD not only enables early decision-making while ensuring accuracy but also achieves accuracy levels comparable to or even surpassing those obtained using complete data, with the added benefit of privacy protection. © 2023 IEEE.

关键词： Decision making

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A Survey of SDN Data Plane Attacks and Defense Strategies 23

A Survey of SDN Data Plane Attacks and Defense Strategies

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2nd International Conference on networks, Communications and information Technology, CNCIT 2023

作者： Zhang, Wenxiu Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798400700620

The Software-Defined networking (SDN) paradigm proposes the decoupling of control and data planes and a centralized software-oriented management approach based on a central controller, easing the development of new applications and services, and fundamentally changed the network architecture. This paper introduces the present situation of SDN and its data plane research, but as with traditional networks, there are multiple vulnerabilities in the security aspects of the SDN architecture, which can be used to perform malicious operations with these vulnerabilities to affect the network and its services. In this paper, the SDN data plane security issues are mainly discussed, the attacks on the SDN data plane are summarized, and the existing attack defense policies are summarized and compared. Finally, the research direction of SDN data plane security is proposed and the whole paper is summarized. CCS CONCEPTS •Security and privacy •network security •Security protocols © 2023 ACM.

关键词： information management

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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TM-HOL: Topic memory model for detection of hate speech and offensive language

TM-HOL: Topic memory model for detection of hate speech and ...

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作者： Chen, Jing Ma, Kun Ji, Ke Chen, Zhenxiang College of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

In the era of the explosion of digital content of large-scale self-media, user-friendly social platforms such as Twitter and Facebook, provide opportunities for people to express their ideas and opinions freely. Due to lack of restrictions, hateful speech and its exposure can have profound psychological impacts on society. Current social networking platform is over-reliant on the manual check, and it is labor-intensive and time-consuming. Although there are many machines learning methods for the detection of hate speech, short text with character limit on social platforms is more challenging for the detection of hate speech and offensive language. To address the problem of data sparsity, we have proposed a topic memory model for hate speech and offensive language detection (abbreviated as TM-HOL). Potential topics are generated with our encoder and decoder to enrich short text features. Two memory matrices correspond to the topic words and the text, and the hate feature matrix is used to learn the syntactic features. It is demonstrated that our proposed method is effective on three datasets, performing better weighted-F1. © 2021 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Joint Learning Based on Discriminant Representation and Group Collaborative Measurement for Image Set Classification 2nd

Joint Learning Based on Discriminant Representation and Grou...

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2nd International Conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Liu, Xingrui Feng, Zeming Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819909223

In daily life, it is easy to obtain a large number of images in the form of sets. How to extract discriminative information from a wide range of images become very important. Most of the existing image set classification methods based on collaborative representation assume that the images in the same set can affect each other, ignoring the interaction between sets. In order to solve the above problems, a joint learning based on discriminative representation and group collaborative measurement (DRGCM) is proposed. Through the deep subspace clustering algorithm, the set is accurately divided, and multiple local models are found to form a dictionary. In the training stage, a global projection matrix learning model is designed to make the dictionary have stronger representation ability of prototype points in the embedded space. In the testing phase, a class-specific projection matrix model is constructed around the test set to make the dictionary more discriminative to the test set in a specific embedding space. Finally, the excellent performance on Honda, Mobo and other database proves the effectiveness of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Time Series Anomaly Detection Based on Normalized Flow and Bayesian networks 9

Time Series Anomaly Detection Based on Normalized Flow and B...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yao, Honglei Liu, Donglan Chang, Yingxian Liu, Xin Tang, Chaofan State Grid Shandong Electric Power Research Institute Jinan China State Grid Shandong Electric Power Company Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Time series anomaly detection often faces significant challenges due to the high dimensionality, interdependence, and label sparsity of the data. Traditional methods struggle to model the intricate relationships between multiple time series components and often fail to accurately detect anomalies due to insufficient data dependence mining. To address these issues, we introduce GCRNF, a novel Graph-Enhanced Standardized Flow Model that incorporates a Bayesian network for causality modeling. This model decomposes joint probabilities into conditional probabilities and utilizes a graph dependency encoder with hybrid CNN-RNN models and Graph Convolutional networks (GCNs). Our experimental results demonstrate that GCRNF outperforms existing methods in density estimation, anomaly detection, and drift identification within time series distributions. Specifically, GCRNF showed a significant improvement in detection accuracy and robustness compared to traditional methods, providing a more effective solution for identifying anomalies in complex multivariate time series data. © 2024 IEEE.

关键词： Markov processes

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CurMIM: Curriculum Masked Image Modeling

CurMIM: Curriculum Masked Image Modeling

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked Image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked Image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 IEEE.

关键词： Curriculum Learning Image Representation Masked Image Modeling Visual Pre-training

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