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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Document Ranking and Retrieval

Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Docu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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Towards Survivable In-Memory Stores with Parity Coded NVRAM

Towards Survivable In-Memory Stores with Parity Coded NVRAM

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhixuan Wang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

关键词： Privacy Nonvolatile memory System performance Redundancy Random access memory Prototypes computer architecture

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Debiasing Graph Representation Learning based on Information Bottleneck

arXiv

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arXiv 2024年

作者： Zhang, Ziyi Ouyang, Mingxuan Lin, Wanyu Lan, Hao Yang, Lei The School of Software Engineering South China University of Technology Guangzhou China The Hong Kong Polytechnic University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Technology Tsinghua University Beijing China

—Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of GRAFair, a new framework based on a variational graph auto-encoder. The crux of GRAFair is the Conditional Fairness Bottleneck, where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Modeling and Analysis for the Propagation of DDoS Attacks in Microgrid System

Modeling and Analysis for the Propagation of DDoS Attacks in...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Lei Xiong Qichao Xu Dongfeng Fang Hui Zeng School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA USA

With the development and progress of electric power technology and information technology, microgrid has become an important and indispensable part of smart grid. The cyber-security of microgrids has a significant impact on the stability and security of smart grids. Distributed denial-of-service (DDoS) attack as one of the common types of cyberattacks in microgrids has formed a considerable threat to the network security of microgrids. In a DDoS event, attackers first use malware to infect few nodes in the microgrid and the infected nodes can generate large-scale malware propagation in the system. These infection lays the foundation for the flood attacks. Therefore, this paper establishes a model to analyse the DDoS attack propagation in microgrid. Firstly, this paper establishs a dynamic model based on the epidemiological theory to describe the propagation dynamics of DDoS attack in microgrid. Then we go on to compute the attack threshold $(R_{0})$ of the model and prove the stability of equilibrium point. Finally we carry out the experimental simulation based on the model, and the experimental results show that the proposed model can reflect the propagation of DDoS attack in microgrid effectively.

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Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition

Distilling Cross-Temporal Contexts for Continuous Sign Langu...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Leming Guo Wanli Xue Qing Guo Bo Liu Kaihua Zhang Tiantian Yuan Shengyong Chen School of Computer Science and Engineering Tianjin University of Technology Centre for Frontier AI Research (CFAR) A*STAR Singapore Walmart Global Tech Sunnyvale CA USA School of Computer and Software Nanjing University of Information Science and Technology Technical College for the Deaf Tianjin University of Technology

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the over-all model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thor-ough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distil-lation learning objective to aggregate the two types of con-text and the linguistic prior. The knowledge distillation en-ables the resultant one-branch temporal aggregation mod-ule to perceive local-global temporal and semantic context. This shallow temporal perception module structure facili-tates spatial perception module learning. Extensive exper-iments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

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A survey of Intel SGX and its applications

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Frontiers of computer science 2021年第3期15卷 191-205页

作者： Wei ZHENG Ying WU Xiaoxue WU Chen FENG Yulei SUI Xiapu LUO Yajin ZHOU School of Software and Microelectronics Northwestern Polytechnical UniversityXi'an 710129China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

This paper presents a comprehensive survey on the development of Intel SGX(software guard extensions)processors and its *** the advent of SGX in 2013 and its subsequent development,the corresponding research works are also increasing *** order to get a more comprehensive literature review related to SGX,we have made a systematic analysis of the related papers in this *** first search through five large-scale paper retrieval libraries by keywords(i.e.,ACM Digital Library,IEEE/IET Electronic Library,SpringerLink,Web of science,and Elsevier science Direct).We read and analyze a total of 128 SGX-related *** first round of extensive study is conducted to classify *** second round of intensive study is carried out to complete a comprehensive analysis of the paper from various *** start with the working environment of SGX and make a conclusive summary of trusted execution environment(TEE).We then focus on the applications of *** also review and study multifarious attack methods to SGX framework and some recent security improvements made on ***,we summarize the advantages and disadvantages of SGX with some future research *** hope this review could help the existing and future research works on SGX and its application for both developers and users.

关键词： Intel SGX cloud computing trusted execution environment TrustZone AMD SEV

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chang-Long Sun Xin-Xin Xu Chun-Hua Chen Jun Hong Zhenan He Dengxiu Yu Sam Kwong Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Technology Ocean University of China Qingdao China School of Software Engineering South China University of Technology Guangzhou China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Department of Computing and Decision Science Lingnan University Hong Kong SAR College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations.

关键词： Costs Scalability Ecosystems Nash equilibrium Electric vehicles Batteries Smart grids Optimization Cybernetics Genetic algorithms

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Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization

arXiv

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arXiv 2024年

作者： Ma, Zeyuan Guo, Hongshu Gong, Yue-Jiao Zhang, Jun Tan, Kay Chen The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Nankai University Tianjin China Hanyang University Seoul Korea Republic of Hong Kong Polytechnic University Hong Kong

In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient summaries of its key aspects and lacks practical guidance for implementation. To bridge this gap, we offer a comprehensive review of recent advances in MetaBBO, providing an in-depth examination of its key developments. We begin with a unified definition of the MetaBBO paradigm, followed by a systematic taxonomy of various algorithm design tasks, including algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Further, we conceptually summarize different learning methodologies behind current MetaBBO works, including reinforcement learning, supervised learning, neuroevolution, and in-context learning with Large Language Models. A comprehensive evaluation of the latest representative MetaBBO methods is then carried out, alongside an experimental analysis of their optimization performance, computational efficiency, and generalization ability. Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO. Finally, we outline the vision for the field by providing insight into the latest trends and potential future directions. Relevant literature will be continuously collected and updated at https://***/GMC-DRL/Awesome-MetaBBO. © 2024, CC BY.

关键词： Self-supervised learning

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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