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arXiv 2017年

作者： He, Xu Liu, Bin Chen, Ke-Jia School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithmrecommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and may lead to misleading recommendations in practice. A natural countermeasure is to design a trust-aware CF algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improvedCF algorithmtermed iTrace is proposed, which employs both the explicit and the predicted implicit trust to provide recommendations. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error. Copyright © 2017, The Authors. All rights reserved.

关键词： Collaborative filtering

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On link formation in heterogeneous information networks: A view based on multi-Label learning 9

On link formation in heterogeneous information networks: A v...

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9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Chen, Ke-Jia Xue, Shijun Li, Yun Liu, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Center for the Neural Basis of Cognition Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML3P is proposed. In ML3P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML3P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships. © 2017 Association for Computing Machinery.

关键词： Forecasting

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Implementation and performance op timization of dynamic random forest

Implementation and performance op timization of dynamic rand...

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第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Xiaolong Xu Wen Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and

Bernard combines the weight updating of the boosting algorithm with the Random Forest(RF),and proposes a new RF induction algorithm called Dynamic Random Forest(DRF).The idea with DRF is to grow only trees that would fit the sub-forest already built,use the existing forest to update the weight of each randomly selected training instance,force the next tree to pay attention to those samples that can not be classified well by the current forest,thus improving the RF ***,this weight updating method is still flawed,which does not make a good distinction between the samples classified correctly and the samples classified wrongly by the current *** this paper,we implement the DRF algorithm,and propose a new weight update method,that is,giving higher weight to the samples classified wrongly by the current forest,giving lower weight to the samples classified correctly by the current forest,so that the next tree will be more concerned with those misclassified *** results show that our method is better than DRF algorithm and traditional RF algorithm.

关键词： Random forest Boosting Dynamic random forest Weight updating

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SCALABLE ATTRIBUTION OF ADVERSARIAL ATTACKS VIA MULTI-TASK LEARNING

arXiv

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arXiv 2023年

作者： Guo, Zhongyi Han, Keji Ge, Yao Ji, Wei Li, Yun Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., attack algorithm, victim model and hyperparameter. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems

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security and Communication Networks 2023年第1期2023卷

作者： Sun, Lei Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

As a key resource for diagnosing and identifying problems, network syslog contains vast quantities of information. And it is the main source of data for anomaly detection of systems. Syslog presents the characteristics of large scale, diverse types and sources, data noise, and quick evolvement, which makes the detection methods not generic enough. To effectively address problem of log anomaly labelling caused by massive heterogeneous logs, we propose LogPal, a generic anomaly detection scheme of heterogeneous logs for network systems, which innovatively combines template sequences and raw log sequences to construct and generate log pattern events. By improving the self-attention mechanism of transformer, LogPal proactively synthesizes self-attention and handles log pattern events in a unique way. The model can make full use of log template and sequence semantic information, by automatically becoming aware of the pattern of logs. We implemented experiments to evaluate the performance of LogPal on publicly available datasets, and the outcome of the experiments shows that LogPal automatically adapts to log type changes and improves precision, recall, and F1 score to 99% on publicly available datasets. © 2023 Lei Sun and Xiaolong Xu.

关键词： Anomaly detection

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MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

arXiv

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arXiv 2023年

作者： Long, Xianzhong Peng, Chen Li, Yun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets. © 2023, CC BY.

关键词： Supervised learning

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Recommendation system based on trusted relation transmission

Recommendation system based on trusted relation transmission

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yixiong Bian Huakang Li Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.

关键词： Collaboration Reliability Social network services Heuristic algorithms Optimization System performance Convergence

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Using deep learning to recognize biomedical entities

Using deep learning to recognize biomedical entities

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xuemin Yang Zhifei Zhang Ronggen Yang Daoyu Huang Geng Yang Lejun Gong Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.

关键词： Machine learning Neural networks Text mining Dictionaries Distributed databases Biological system modeling Telecommunications

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Dafl: Domain Adaptation-Based Federated Learning for Privacy-Preserving Biometric Recognition

SSRN

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SSRN 2023年

作者： Wang, Zhousheng Yang, Geng Dai, Hua Bai, Yunlu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big data Security & Intelligent Processing Nanjing210023 China

With the introduction of data protection regulation in various countries, traditional centralized learning for the exploitation of sensitive biological information will gradually become unsustainable. We take face and speaker recognition systems as examples, to consider the possibility of implementing them using federated learning. It is known that federated learning methods still have some issues to be solved, e.g., parameter updates delivered can still reveal user privacy. Besides, existing privacy-preserving methods such as encryption and perturbation could pose other problems, including low accuracy and high training overhead, which can be more prominent in biometric recognition scenarios. We adopt the idea of domain adaptation and propose a new federated learning framework, DAFL, focusing on the improvement of the training efficiency and model performance, to achieve a practical privacy-preserving biometric recognition scheme for the first time. We also design a special dropout method for it to address the possible imbalance of biometric samples across users. Experiments on real-world datasets show that our approach can achieve up to 95.6% and 97.9% accuracy in face recognition and speaker recognition tasks, respectively, under medium privacy settings. Compared with existing work, it can save 56.0% of training time with a 3.1% accuracy loss in the best case. © 2023, The Authors. All rights reserved.

关键词： Biometrics

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