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arXiv 2023年

作者： Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Recently, Graph Neural networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Challenges and Advances in Generative Information Retrieval 22

Challenges and Advances in Generative Information Retrieval

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22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Fan, Yixing Tang, Yubao Chen, Jiangui Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781713889892

Information retrieval (IR) aims to seek relevant information in response to user queries. Existing IR systems mainly rely on the "index-retrieve-then-rank" framework, which models the complex retrieval tasks as a multi-stage search process. Such a decoupling process improves the efficiency of the system, making it possible for retrieval system to handle billions of documents. However, it also increase the complexity of the search architecture, making it difficult to achieve end-to-end optimization. To address this issue, researchers have begun to explore a new paradigm of generative information retrieval. This new paradigm encodes the entire corpus into the search model, enabling end-to-end optimization and eliminating the dependence on external indices. Currently, generative information retrieval has become a hot research direction in IR, and researchers have proposed different solutions to improve retrieval effectiveness. Given the rapid progress in this direction, this article provides a systematic review of generative information retrieval, including basic concepts, document identifiers, model architectures, and model capacity. In addition, we also discuss some unresolved challenges and promising research directions, hoping to inspire and promote future research on these topics. © 2023 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Security Enhancing for IRS-Aided NOMA networks via Constructive Interference Precoding 10

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Bao, Jingying Cao, Yang Qin, Xiaoqi Xu, Lexi Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore487372 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Institute China United Network Communications Corporation Beijing100048 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798331533922

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA. © 2024 IEEE.

关键词： Multiple access interference

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Text Information Mining in Cyberspace: An Information Extraction Method Based on T5 and keyBERT 9

Text Information Mining in Cyberspace: An Information Extrac...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Liu, Kuan Li, Yumei Qi, Yuanhua Qi, Ning Zhai, Mengran Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Institute of Economy and Informatization Development Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan 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 SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (纸本)9798350391367

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value. © 2024 IEEE.

关键词： Spatio-temporal data

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Information and Sensing Beamforming Optimization for Multi-User Multi-Target MIMO ISAC Systems 48

Information and Sensing Beamforming Optimization for Multi-U...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, IcasSP 2023

作者： Zhu, Minghe Li, Lei Xia, Shuqiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen China Shenzhen Research Institute of Big Data China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9781728163277

In this paper, we consider the joint beamforming design for simultaneous sensing and communication in a wireless multi-user system. Different from the existing works that mostly are for single target, we consider sensing the channel parameters of multiple targets while communicating with multiple users. The design goal is to minimize a weighted sum of the Cramer-Rao bounds (CRB) of target parameters subject to the communication sum rate and transmission power constraints. While the classical weighted minimum mean square error (WMMSE) and semidefinite relaxation (SDR) can be used to handle the problem, we propose to reformulate the problem into a max-min form, by leveraging the tightness of SDR, and solve it by a low-complex first-order method. Numerical results not only demonstrate the computation efficiency of the proposed algorithm but also its effectiveness in enhancing the sensing performance in practice. © 2023 IEEE.

关键词： Mean square error

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D4M: dataset Distillation via Disentangled Diffusion Model

D4M: Dataset Distillation via Disentangled Diffusion Model

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Duo Su Junjie Hou Weizhi Gao Yingjie Tian Bowen Tang UCAS School of Computer Science and Technology Research Center on Fictitious Economy and Data Science CAS Key Laboratory of Big Data Mining and Knowledge Management CAS UCAS Sino-Danish College Department of Computer Science NCSU UCAS School of Economics and Management UCAS MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation CAS Institute of Computing Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D 4 M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D 4 M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects.

关键词： Training Computer vision Computational modeling Prototypes Computer architecture Diffusion models Pattern recognition

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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The Adaptive Fault-tolerant Routing Based on an Improved Local Security Information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud computing, 16th IEEE International Conference on Social computing and networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Unlocking the Power of Large Language Models for Entity Alignment

arXiv

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arXiv 2024年

作者： Jiang, Xuhui Shen, Yinghan Shi, Zhichao Xu, Chengjin Li, Wei Li, Zixuan Guo, Jian Shen, Huawei Wang, Yuanzhuo CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China School of Computer Science and Technology University of Chinese Academy of Science China IDEA Research International Digital Economy Academy China

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs’ capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results verify ChatEA’s superior performance, highlighting LLMs’ potential in facilitating EA tasks. The source code is available at https://***/IDEA-FinAI/ChatEA. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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