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Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation

Graph Contrastive Learning with Adaptive Augmentation for Kn...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Mei Yu Zhaoyuan Ding Jian Yu Wenbin Zhang Ming Yang Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Knowledge concept recommendation is a kind of fine-grained recommendation in massive open online courses (MOOCs) scenario, user interaction data has the characteristics of strong collaborative signals and imbalanced interactions. This leads to a single recommendation and reduced accuracy. Recently, the ability of contrastive learning (CL) in mitigating interaction imbalance in recommender systems has received widespread attention. CL requires the use of augmentation methods to generate different views. Existing augmentation methods (1) augment only at the topology or feature level ignoring semantic or structural information, and (2) undifferentiated augmentation tends to lose the critical information. In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec). Specifically, (1) topology level adaptive augmentation based on degree centrality captures critical structural information, and then (2) feature level adaptive augmentation based on degree centrality captures critical semantic information. Comprehensive experiments show that our proposed approach can outperform other baselines. Our implementations are available at https://***/DingZhaoyuan/GCARec.

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Combination of Translation and Rotation in Dual Quaternion Space for Temporal Knowledge Graph Completion

Combination of Translation and Rotation in Dual Quaternion S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ruiguo Yu Tao Liu Jian Yu Wenbin Zhang Yue Zhao Ming Yang Mankun Zhao Jiujiang Guo College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Compared with static knowledge graphs (KGs) temporal KGs record the dynamic relations between entities over time, therefore, research on temporal Knowledge Graph Completion (KGC) attracts much attention. Temporal KGs exhibit complex temporal relation patterns, such as multiple relations. However, existing methods can hardly model all the relation patterns and apply to the temporal KGs. In this paper, we propose a novel temporal KGC method that Combining Translation and Rotation (ComTR) in Dual Quaternion Space for temporal KGC. Specifically, we use dual-quaternion-based multiplication to model timestamps and relations as the combination of translation and rotation operations. We analyze the relation patterns of temporal KGs in detail and demonstrate that our method can model all the relation patterns in temporal KGs. Empirically, we show that ComTR can achieve the state-of-the-art performances over four temporal KGC benchmarks datasets.

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MASZSL: A Multi-Block Attention-Based Description Generative Adversarial Network for Knowledge Graph Zero-Shot Relational Learning

MASZSL: A Multi-Block Attention-Based Description Generative...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Mei Yu Pengtao Fan Mankun Zhao Wenbin Zhang Yue Zhao Ming Yang Jian Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

In the real world, the Knowledge Graph(KG) is dynamic and new entities are added at any time. Therefore, open-world Knowledge Graph Completion(KGC) was proposed to approach new-added entities, but previous approaches often introduced too much noise when introducing external text resources for new entities. To alleviate this problem, knowledge graph zero-shot relational learning (KGZSL) has recently attracted more attention. Generative Adversarial Networks (GANs) are frequently used in KGZSL to connect existing relation descriptions to the domain of knowledge graphs. However, these methods ignore the impact of existing entities on embeddings for unidentified relational representations and in-stead concentrate on examining the connection between relational textual texts and knowledge network structures. In this work, we propose a multi-block attention framework using relation Description Generative Adversarial Networks (desGAN) jointing KG and text representation and address model collapse and training stability problems in previous studies. The core idea of our method is to obtain the background knowledge graph information and the relation representation through and multi-block attention layer and the desGAN, then a connection between the structured KG semantic space and the unstructured text semantic space of the new entity is established, forcing the entity pair to be closer to their real relation. Experimental results on the knowledge graph zeroshot relational learning dataset demonstrate that our MASZSL has a faster convergence speed and achieves state-of-the-art performance on this task.

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A Software Diversity-Based Lab in Operating System for Cyber Security Students 3

A Software Diversity-Based Lab in Operating System for Cyber...

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3rd IEEE International Conference on Computer Science and Educational Informatization, CSEI 2021

作者： Wang, Junchao Pang, Jianmin Shan, Zheng Wei, Jin Yao, Jinyang Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng Zhou China School of Computer Science Data Arena Institute Fudan University Zheng Zhou China

ISBN: (纸本)9781728184739

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students. © 2021 IEEE.

关键词： Students

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Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model 7th

Multi-objective Firefly Algorithm for Test Data Generation w...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Zhang, Wenning Zhou, Qinglei Jiao, Chongyang Xu, Ting State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China Zhongyuan University of Technology Zhengzhou450000 China Zhengzhou University Zhengzhou450000 China

ISBN: (纸本)9789811659393

To solve the emerging complex optimization problems, multi objective optimization algorithms are needed. By introducing the surrogate model for approximate fitness calculation, the multi objective firefly algorithm with surrogate model (MOFA-SM) is proposed in this paper. Firstly, the population was initialized according to the chaotic mapping. Secondly, the external archive was constructed based on the preference sorting, with the lightweight clustering pruning strategy. In the process of evolution, the elite solutions selected from archive were used to guide the movement to search optimal solutions. Simulation results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Imperceptible and Reliable Adversarial Attack 1

引用

4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 21

Distributed Deep Learning System for Efficient Face Recognit...

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Proceedings of the 3rd International Conference on advanced Information Science and System

作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning

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A software upgrade security analysis method on network traffic classification using deep learning

A software upgrade security analysis method on network traff...

引用

2020 International Conference on Urban engineering and Management Science, ICUEMS 2020

作者： Zhang, Bing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9781728188324

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%. © 2020 IEEE.

关键词： Neural network models

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The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

The Executors Scheduling Algorithm for the Web Server Based ...

引用

2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Nie, Guanglai Zhang, Zheng Zhao, Yufeng Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728165202

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm. © 2020 IEEE.

关键词： Scheduling algorithms

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An evaluation algorithm of the importance of network node based on community influence 1

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5th International Conference on Data Mining and Big Data, DMBD 2020

作者： He, Gongzhen Luo, Junyong Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (数字)9789811572050

ISBN: (纸本)9789811572043

Identifying nodes in social networks that have great influence on information dissemination is of great significance for monitoring and guiding information dissemination. There are few methods to study the influence of communities on social networks among the existing node importance evaluation algorithms, and it is difficult to find nodes that promote information dissemination among communities. In view of this reason, this paper proposes a node importance evaluation algorithm based on community influence (abbreviated as IEBoCI algorithm), which evaluates the importance of the nodes based on the influence degree of the nodes on the communities and the ability to disseminate information the communities to which the nodes are connected. This algorithm firstly calculates the activation probability of nodes to other nodes, which is used to divide communities and evaluate influence. Secondly, the network is divided into communities based on LPA algorithm. Finally, the importance of the node is calculated by combining the influence of the community itself and the influence of the node on the community. Experiments are carried out on real social network data and compared with other community-based methods to verify the effectiveness of the algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Complex networks

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