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arXiv 2020年

作者： Zhu, Lixing He, Yulan Zhou, Deyu Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Superpixel-guided discriminative low-rank representation of hyperspectral images for classification

arXiv

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arXiv 2021年

作者： Yang, Shujun Hou, Junhui Jia, Yuheng Mei, Shaohui Du, Qian The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Electronics and Information Northwestern Polytechnical University Xian710129 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is mainly composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted. Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels. According to the resulting superpixels, the pixels of the input HSI are then grouped into clusters and fed into our novel discriminative low-rank representation model with an effective numerical solution. Such a model is capable of increasing the intra-class similarity by suppressing the spectral variations locally while promoting the inter-class discriminability globally, leading to a restored HSI with more discriminative pixels. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods, especially for the case with an extremely limited number of training pixels. © 2021, CC BY.

关键词： Spectroscopy

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Revision of stratified OWL ontologiesbased on integer linear programming

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Journal of Southeast University(English Edition) 2020年第1期36卷 1-7页

作者： Ji Qiu Qi Guilin Boutouhami Khaoula School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing 210023 China School of Computer Science and Engineering Southeast University Nanjing 211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing 211189 China

To revise stratified web ontology language(OWL)ontologies,the kernel revision operator is extended by defining novel conflict stratification and the incision function based on integer linear programming(ILP).The ILP-based model considers an optimization problem of minimizing a linear objective function which is suitable for selecting the minimal number of axioms to remove when revising *** on the incision function,a revision algorithm is proposed to apply ILP to all minimal incoherence-preserving subsets(MIPS).Although this algorithm can often find a minimal number of axioms to remove,it is very time-consuming to compute ***,an adapted revision algorithm to deal with unsatisfiable concepts individually is also *** results reveal that the proposed ILP-based revision algorithm is much more efficient than the commonly used algorithm based on the hitting set *** addition,the adapted algorithm can achieve higher efficiency,while it may delete more axioms.

关键词： ontology revision inconsistency handling semantic web integer linear programming

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AAR-Log: A robust log anomaly detection method resisting adversarial attacks

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computer networks 2025年

作者： Jiahao Wu Sanfeng Zhang Hongxian Liu Wang Yang School of Cyber Science and Engineering Southeast University No. 2 Southeast University Road Nanjing 211189 Jiangsu China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education No. 2 Southeast University Road Nanjing 211189 Jiangsu China

In recent years, adversarial evasion attacks against log-based anomaly detection systems have been proven to pose severe threats. Existing detection models lack targeted defense mechanisms against such attacks, enabling malicious actors to conceal anomalous activities. This not only prevents timely detection of system failures or intrusions, but also significantly elevates security risks and potential losses. To address this challenge, this paper proposes AAR-log, a novel adversarial-resilient log anomaly detection framework. The key innovations of AAR-log include: (1) integrating various types of features including log components, log levels, and log templates to construct a more comprehensive representation of log sequences; (2) employing ensemble learning to enhance model diversity and mitigate the vulnerability of single-model approaches; and (3) incorporating adversarial training to constrain the adversarial sample space, thereby significantly improving the framework’s robustness under adversarial conditions. Extensive experiments on benchmark log datasets demonstrate that AAR-log exhibits superior adversarial robustness. Its ensemble learning mechanism and adversarial training strategy effectively enhance resistance against evasion attacks, achieving higher TPR and F1 scores compared to baseline models. Notably, AAR-log also maintains strong generalization performance in non-adversarial environment. In the HDFS dataset, AAR-log improves TPR by 2.8%–9.5% and F1-score by 4.6%–14.3% over baseline models. In the BGL dataset, it achieves even greater gains, increasing TPR by 17.1%–26.7% and F1-score by 3.9%–28.4%. These results validate the effectiveness and robustness of AAR-log in log-based anomaly detection.

关键词：

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Thin Semantics Enhancement via High-Frequency Priori Rule for Thin Structures Segmentation

Thin Semantics Enhancement via High-Frequency Priori Rule fo...

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IEEE International Conference on Data Mining (ICDM)

作者： Yuting He Rongjun Ge Jiasong Wu Jean-Louis Coatrieux Huazhong Shu Yang Chen Guanyu Yang Shuo Li LIST Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China Centre de Recherche en Information Biomédicale Sino-Français (CRIBs) Univ Rennes Inserm LTSI - UMR1099 Rennes F-35000 France University of Western Ontario London ON Canada

ISBN: (纸本)9781665423991

Receptive field-based segmentation models represent features in receptive fields having weak perception for thin semantics in thin structures segmentation, due to the challenges in small local size and large global variation. High-frequency (HiFe) components have strong thin perception ability and is stable for global variation, but its weak adaptability limits its direct application. We propose a HiFe priori rule which enables the network to adaptively extract and fuse HiFe components, enhancing the thin semantics and making the network naturally prefer thin structures for their segmentation. We further propose High-Frequency Semantics Enhancement network (HiFeNet) based on our HiFe priori rule, boosting the SOTA methods in thin structures segmentation: 1) Our Deep High Frequency (DHiFe) block learns to extract task-dependent HiFe components and adds them to feature maps, achieving great perception of thin structures. 2) Our Latent Residual Denoising (LRD) block progressively weakens task-independent features via hierarchical residuals and learns to fuse HiFe components back to feature maps, further enhancing the thin semantics and weakening the interference of global variation. Extensive experiments on the retinal vessel [1], [2], [3] and Massachusetts road [4] segmentation datasets show great superiority of our HiFeNet.

关键词： Wavelet transforms Fuses Roads Semantics Noise reduction Interference Feature extraction

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Default mode network static-dynamic functional signatures in first-episode drug-naive major depressive disorder

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Comprehensive Psychoneuroendocrinology 2025年 23卷

作者： Xiaoyun Liu Kaimo Ding Fan Li Xiaotong Zhang Zhenghua Hou Yingying Yin Youyong Kong Yonggui Yuan Department of Psychosomatics and Psychiatry Zhongda Hospital School of Medicine Jiangsu Provincial Key Laboratory of Brain Science and Medicine Southeast University Nanjing China Zhenjiang Mental Health Center Jiangsu Province Zhenjiang China Lab of Image Science and Technology School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

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Parallel Sketch Based Super Node Detection with Traceability

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Chinese Journal of Electronics 2018年第6期27卷 1133-1140页

作者： ZHOU Aiping LIU Lijun ZHU Huisheng ZHU Chen'gang CHEN Lin School of Computer Science and Technology Taizhou UniversityTaizhou 225300 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement *** to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability(PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator(CSE) and Data streaming and sampling(DSS) in terms of detection accuracy and storage utilization.

关键词： network measurement Super node detection Parallel sketch network data stream

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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering

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Big Data Mining and Analytics 2018年第3期1卷 211-221页

作者： Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information Integration Southeast University

Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.

关键词： hybrid recommender system neural collaborative filtering auto-encoder implicit feedback

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Towards Efficient Co-audit of Privacy-Preserving Data on Consortium Blockchain via Group key Agreement

Towards Efficient Co-audit of Privacy-Preserving Data on Con...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xiaoyan Hu Xiaoyi Song Guang Cheng Jian Gong Lu Yang Honggang Chen Zhichao Liang School of Cyber Science & Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Research Base of International Cyberspace Governance (Southeast University) Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China JiangSu Peerfintech Technology Co. Ltd.

ISBN: (纸本)9781665406697

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our

关键词： Industries Privacy Data privacy Distributed ledger Public key Prototypes Fabrics

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Multi-label Classification with High-rank and High-order Label Correlations

arXiv

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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