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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. Experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Dual consistent constraint via disentangled consistency and complementarity for multi-view clustering

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Neurocomputing 2025年 641卷

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025

关键词： Contrastive Learning

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TSAR-based Expert Recommendation Mechanism for Community Question Answering

TSAR-based Expert Recommendation Mechanism for Community Que...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jian Song Xiaolong Xu Xinheng Wang Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computing and Engineering University of West London London UK

ISBN: (纸本)9781728165974;9781728165981

Community Question Answering (CQA) provides a platform to share knowledge for users. With the increasing number of users and questions, askers have to wait a long time for an answer with high quality while responders may not be interested in assigned questions. Current methods usually try to address this issue based on text or link analysis. However, most of them suffer from delayed answers or low coverage of best answer. In this paper, we design a novel expert recommendation mechanism by incorporating the deep structured semantic model (DSSM) [20] with our proposed graph-based algorithm, a topic sensitive answerer rank algorithm (TSAR). In the process of constructing transition probability matrix, we not only take into account both the number of questions answered by the user and question difficulty, but also consider the user's average response time for providing the answer. The experiments carried out on Yahoo! Answers and Stack Overflow datasets demonstrate that the proposed mechanism outperforms the current typical algorithms [9] on multiple metrics and achieves the best answer coverages, which are 61.5% and 53.8%, respectively.

关键词： Measurement Conferences Semantics Knowledge discovery Collaborative work Time factors

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Blockchain Private key Storage Algorithm Based on Image Information Hiding 5th

Blockchain Private Key Storage Algorithm Based on Image Info...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Wang, Ning Chen, Yuling Yang, Yu Fang, Zigang Sun, Yizhe School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

The private key is the unique credential of the blockchain asset and can be used to sign and authenticate transactions. Losing the private key also loses the control of the account, so the leakage of the private key is one of the major factors affecting the security of the blockchain. In order to improve the security of blockchain private key storage technology in transactions, this paper proposes a blockchain private key storage algorithm based on image information hiding. In order to improve the camouflage ability and reduce the bit error rate, the algorithm adds random numbers and error correction code to the original private key to provide double protection. After generating the watermark information, the watermark information is embedded into the digital image by means of discrete wavelet transform and quantized index modulation to realize the process of information hiding. Finally, it is proved that this algorithm has good transparency and robustness, can hide the private key information visually, and extract it accurately. © 2019, Springer Nature Switzerland AG.

关键词： Blockchain

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Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

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Heuristic-Q: A Privacy data Pricing Method Based on Heuristic Reinforcement Learning 5th

Heuristic-Q: A Privacy Data Pricing Method Based on Heuristi...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Chen, Xi Chen, Jingjie Chen, Yuling Yang, Jinglan Li, Deyin School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data Guizhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

With the development of big data applications in recent years, the value of personal data has received more and more attention. How to balance the conflict between data value development and personal privacy protection is an urgent problem to be solved. In response to the above contradictions, the market proposed a private data transaction mechanism to realize the commercial use of private data. However, existing private data transaction mechanisms still have deficiencies in some respects. In order to seek pricing strategies that maximize total revenue, this paper designs and constructs a privacy data dynamic pricing model from the perspective of data collectors, introduces heuristic reinforcement learning ideas, and proposes a pricing strategy algorithm based on heuristic functions. Finally, the simulation experiment of the proposed pricing model and strategy learning algorithm is carried out, and its performance is analyzed. The simulation results confirm that the algorithm can help data collectors get higher returns in the process of limited private data transactions. © 2019, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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PPCSB: A Privacy-Preserving Electricity Consumption Statistics and Billing Scheme in Smart Grid 5th

PPCSB: A Privacy-Preserving Electricity Consumption Statisti...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Li, Chenyang Chen, Yuling Yang, Yu Li, Chaochao Zeng, Yang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

Research on the privacy protection of smart grids mostly stays in fixed electricity prices or electricity aggregation. Aiming at the problem of real-time pricing and privacy data protection of smart grid, this paper proposes a privacy-preserving electricity consumption statistics and billing scheme in smart grid (PPCSB). In this scheme, the techniques of additive homomorphic encryption and mixed multiplicative homomorphic encryption are used to ensure the security of the data communication;Batch verification is used to improve the efficiency of signature verification. In addition, the proposed scheme does not require a trusted third party in order to improve the usability of the scheme. Through performance analysis of the scheme, it shows that the scheme has better security and better functions. © 2019, Springer Nature Switzerland AG.

关键词： Smart power grids

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Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering

arXiv

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arXiv 2025年

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view’s complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025, CC BY.

关键词： Contrastive Learning

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Multi-objective optimization for fuzzy workflow scheduling

Multi-objective optimization for fuzzy workflow scheduling

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jie Zhu Jing Zhang Chang Lu Haiping Huang Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China Nanjing University of Posts & Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665442084

A fuzzy workflow scheduling problem is investigated with fuzzy temporal parameters, such as the fuzzy task processing times, fuzzy data transmission times and the fuzzy due dates. In the considered problem, the resources are elastic cloud resources with multiple price structures. Due to the fuzzy temporal parameters, the deadline constraint cannot be ensured. Therefore, we formulate a triangle fuzzy number-based workflow scheduling problem model with the soft deadline constraint. A greedy local search method is proposed. The objectives are minimizing the total rental cost and the average dissatisfaction degree. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the two existing algorithms.

关键词： Measurement Schedules Costs Processor scheduling Search methods Proposals data communication

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A Method of Network Topology Discovery Based on Knowledge Reasoning : Based on more than heterogeneous data sources

A Method of Network Topology Discovery Based on Knowledge Re...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Jun Xu Yang Xin Runcheng Liu Cyberspace Security Institute School of Beijing University of Posts and Telecommunications Beijing China Cyberspace Security Institute Beijing University of Posts and Telecommunications Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou China

ISBN: (纸本)9781728186689;9781728186696

At present, the network has gradually become the foundation of information construction. People have more demands for network topology management. The increasing scale of network topology and the heterogeneity between devices and protocols have brought great difficulties to network topology discovery. This paper proposes a knowledge-based reasoning Network topology discovery method. This method uses multiple heterogeneous data sources, uses computer network prior knowledge to construct a knowledge base from top to bottom, uses inference learning to establish reasoning rules, and then constructs a multi-level network topology structure.

关键词： Knowledge engineering Couplings Protocols Network topology databases Fuses Knowledge based systems

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