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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yi, Xiaoyang Bao, Yuru Zhang, Jian Qin, Yifang Lin, Faxin College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China

ISBN: (纸本)9798891761643

Information Extraction (IE), aiming to extract structured information from unstructured natural language texts, can significantly benefit from pre-trained language models. However, existing pre-training methods solely focus on exploiting the textual knowledge, relying extensively on annotated large-scale datasets, which is labor-intensive and thus limits the scalability and versatility of the resulting models. To address these issues, we propose SKIE, a novel pre-training framework tailored for IE that integrates structural semantic knowledge via contrastive learning, effectively alleviating the annotation burden. Specifically, SKIE utilizes Abstract Meaning Representation (AMR) as a low-cost supervision source to boost model performance without human intervention. By enhancing the topology of AMR graphs, SKIE derives high-quality cohesive subgraphs as additional training samples, providing diverse multi-level structural semantic knowledge. Furthermore, SKIE refines the graph encoder to better capture cohesive information and edge relation information, thereby improving the pre-training efficacy. Extensive experimental results demonstrate that SKIE outperforms state-of-the-art baselines across multiple IE tasks and showcases exceptional performance in few-shot and zero-shot settings. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Smart surgical telementoring system with patch-to-pixel point tracking method

EngMedicine

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EngMedicine 2025年第1期2卷

作者： Yanwei Qu Ling Li Tongtong Li Jieyu Zheng Wentao Tang Xiaojian Li School of Management Hefei University of Technology Hefei 230009 Anhui China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Hefei 230009 Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 Anhui China Hefei Haochuan Information Technology Co. Ltd Hefei 230088 Anhui China Ministry of Education Engineering Research Center for Intelligent Decision-Making Information System Technologies Hefei 230009 Anhui China

The utilization of surgical telementoring has become increasingly prevalent in enhancing the surgical standards of grassroots hospitals in contemporary times. In the traditional framework of surgical telementoring, remote doctors guide grassroots doctors via video and audio communication, which may encounter obstacles such as inadequate information transfer and challenges in accurately pinpointing the surgical site and path. To mitigate these issues, this study introduces an intelligent surgical telementoring system based on edge computing. This system enables the transmission of points marked by remote doctors to grassroots doctors, updating these points’ coordinates in real-time on the local endoscopic video. In this system, a novel method named P2PTracking (Patch-to-Pixel Point Tracking) is implemented. This process begins with the tracking of a square patch surrounding the point marked by the remote doctor using SiameseFC. Following this, feature matching is performed on the tracked patch in the current frame and the square patch in the labelled frame or template frame. The affine transformation is then calculated based on the feature matching results. Lastly, the point tracking result is derived using the computed affine transformation. Experimental results indicate that the proposed system has a transmission speed of 1.99M/s and a transmission latency of 171 ms when transmitting video at a resolution of 1920 × 1080px, while the proposed method can achieve an accuracy of 96.6 % when the pixel error is 4. The code and data are available at https://***/hfut66/*** .

关键词： Surgical telementoring Patch tracking Point tracking Feature matching Template update mechanism

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Giant negative ECE near room temperature and large positive ECE of CoFe 2 O 4 -BiFeO 3 /P(VDF-TrFE) sandwich flexible films

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Applied Surface Science 2025年 707卷

作者： Xiang Lv Lingfang Xu Yalong Ge Tian Liang Ronghua Qin Changping Yang Key Laboratory of Intelligent Sensing System and Security of the Ministry of Education Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices School of Microelectronics Hubei University Wuhan 430062 China Hubei Provincial Key Laboratory of Green Materials for Light Industry Hubei University of Technology Wuhan 430062 China Faculty of Materials Science and Engineering Taiyuan University of Science and Technology Taiyuan 030027 China

The electrocaloric effect (ECE) represents an eco-friendly, solid-state cooling technique utilizing a safe and potent electric field to facilitate the refrigeration process. This research demonstrates that incorporating CoFe 2 O 4 (CFO) and BiFeO 3 (BFO) at a 1:1 mass ratio into P(VDF-TrFE) results in notable giant negative ECE at near room temperature and positive ECE in sandwiched composite films. With a 5 wt% addition of CFO and BFO, the composite films exhibited giant adiabatic temperature changes of 11.3 K at 374.4 K and −24 K at 316 K. Using the Landau model, we confirmed the occurrence of both positive and negative ECEs. We predicted an adiabatic temperature change as high as −37.2 K at an electric field of 120 MV m −1 . The exceptional performance of the flexible, sandwiched composite films is attributed to their optimized microstructure and the strengthened local field around the dipoles, a result of the conductive CoFe 2 O 4 and BiFeO 3 nanoparticles. These dual-functional cooling films provide a means to boost the ECE in flexible materials, holding significant promise for applications in solid-state refrigeration tailored for flexible electronics. Future applications of the composite films may extend to various scenarios beyond the known multiferroic properties of BiFeO 3 and the magnetic characteristics of CoFe 2 O 4 .

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Simplifying graph contrastive learning with single feature fusion and column vector orthogonal noise

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Expert systems with Applications 2025年 288卷

作者： Lan You Zhen Zhang Qingxi Peng Christophe Claramunt Xing Zeng Hong Jin School of Computer Science and Information Engineering Hubei University Wuhan430062 Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 School of Information Engineering Wuhan College Wuhan 430212 China Computer Science Naval Academy Research Institute Lanveoc-Poulmic BP 600 Brest 29240 France Okinawa Institute of Science and Technology Japan

Graph Contrastive Learning (GCL) has shown promise in recommendation systems by improving representation quality through data augmentation and consistency objectives. However, conventional GCL frameworks rely on multi-channel augmentation, which introduces significant computational overhead and limits scalability. To address this, we propose Single Feature Fusion Graph Contrastive Learning (S-GCL), a non-parametric and lightweight framework that simplifies contrastive learning by applying single feature fusion to the final prediction embeddings within each mini-batch. To further enhance S-GCL, we introduce Column Vector Orthogonal Noise (CVON), a feature-level augmentation strategy that injects orthogonal noise into each dimensional feature within the embedding space. CVON enriches representations while preserving semantic integrity, and incurs minimal computational overhead. Its integration within the S-GCL framework yields the proposed SCGCL method. Extensive experiments conducted on six publicly available benchmark datasets validate the effectiveness and generalizability of SCGCL. Notably, integrating SCGCL into LightGCN leads to substantial gains in both training efficiency and recommendation accuracy. For example, on the large-scale Alibaba-iFashion dataset, our method improves Recall@20 by 29 % and NDCG@20 by 33 %, while reducing training time by an impressive 84 %.

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FortisAVQA and MAVEN: a Benchmark dataset and Debiasing Framework for Robust Multimodal Reasoning

arXiv

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arXiv 2025年

作者： Ma, Jie Gao, Zhitao Chai, Qi Liu, Jun Wang, Pinghui Tao, Jing Su, Zhou Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510000 China

Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video inputs accurately. However, existing AVQA approaches often suffer from overfitting to dataset biases, leading to poor robustness. Moreover, current datasets may not effectively diagnose these methods. To address these challenges, we first introduce a novel dataset, FortisAVQA, constructed in two stages: (1) rephrasing questions in the test split of the public MUSIC-AVQA dataset and (2) introducing distribution shifts across questions. The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions. Second, we introduce a robust Multimodal Audio-Visual Epistemic Network (MAVEN) that leverages a multifaceted cycle collaborative debiasing strategy to mitigate bias learning. Experimental results demonstrate that our architecture achieves state-of-the-art performance on FortisAVQA, with a notable improvement of 7.81%. Extensive ablation studies on both datasets validate the effectiveness of our debiasing components. Additionally, our evaluation reveals the limited robustness of existing multimodal QA methods. We also verify the plug-and-play capability of our strategy by integrating it with various baseline models across both datasets. Our dataset and code are available at https://***/reml-group/fortisavqa. Copyright © 2025, The Authors. All rights reserved.

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MSpace: Multi security namespace management for containers

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2025年

作者： Liang, Hao He, Gongshan Jiang, Jinhu National Digital Switching System Engineering & Technological Research Center Zhengzhou 450002 China Key Laboratory of Cyberspace Security Ministry of Education Zhengzhou 450002 China School of Computer Science Fudan University Shanghai 200438 China Institute of Big Data Fudan University Shanghai 200433 China

Existing system security frameworks like LSM are primarily designed for the operating system kernel and are not applicable to user-level container methods. This is primarily because the system security framework is designed and implemented within the operating system kernel, which is shared across the entire system and cannot fulfill the isolation requirements of containers. The security policies of multiple containers are intermixed in the underlying system kernel, which impacts each other and increases the overhead of monitoring, leading to inefficiencies in cloud servers running numerous containers. Improvements to the security of containers are ad hoc and partial, often lacking support for the universal SELinux mode. In this paper, we introduce MSpace, an enhanced kernel SELinux framework that supports containerized secure spaces with individual security policies and mechanisms. To enhance security, we first introduce an instantiation mechanism for virtual hook tables, which, in conjunction with the container's namespace, ensures each has its own instantiated hook tables instead of a system-wide one, thereby isolating secure spaces. Subsequently, a security namespace method was devised to support container-specific private security policies. Lastly, to maintain compatibility, only the instantiation and deallocation interfaces were added while preserving the original interfaces unchanged. The results demonstrate that MSpace effectively alleviates the issue of using SELinux for internal containers, with an average reduction of 2.83 % in system call latency compared to using host SELinux directly. © 2025 The Authors

关键词： Container security MSpace SELinux system call latency Virtual jump tables

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NCCMF:Non-Collaborative Continuous Monitoring Framework for Container-Based Cloud Runtime Status

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Computers, Materials & Continua 2024年第10期81卷 1687-1701页

作者： Tao Zheng Wenyi Tang Xingshu Chen Changxiang Shen School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Intelligent Policing and National Security Risk Management Laboratory Luzhou646000China Cyber Science Research Institute Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China

The security performance of cloud services is a key factor influencing users’selection of Cloud Service Providers(CSPs).Continuous monitoring of the security status of cloud services is ***,existing research lacks a practical framework for such ongoing *** address this gap,this paper proposes the first NonCollaborative Container-Based Cloud Service Operation State Continuous Monitoring Framework(NCCMF),based on relevant *** operates without the CSP’s collaboration by:1)establishing a scalable supervisory index system through the identification of security responsibilities for each role,and 2)designing a Continuous Metrics Supervision Protocol(CMA)to automate the negotiation of supervisory *** framework also outlines the supervision process for cloud services across different deployment *** results demonstrate that NCCMF effectively monitors the operational state of two real-world IoT(Internet of Things)cloud services,with an average supervision error of less than 15%.

关键词： Container-based cloud non-collaborative continuous monitor runtime status

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Community discovery method based on complex network of data fusion based on the super network perspective

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International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

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An economic dispatch model based on scenario tree in industrial micro-grid with wind power 27

An economic dispatch model based on scenario tree in industr...

引用

27th Chinese Control and Decision Conference, CCDC 2015

作者： Ding, Hao Gao, Feng Liu, Kun Guan, Xiaohong State Key Laboratory for Manufacturing System Engineering Xi'An Jiaotong University Xi'an China Ministry of Education Key Lab for Intelligent Networks and Networks Security Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9781479970179

Wind power is a kind of clean and renewable energy, and it is of great benefit to utilize it in an industrial micro-grid. This paper establishes an economic dispatch model of industrial micro-grid with wind power that aims at minimizing energy costs based on scenario tree. The uncertainty of wind power and load are managed by adjusting the production schedule and unit generation schedule properly. A numerical test is taken with data of an industrial micro-grid in the paper, and the result indicates that the fluctuation of wind power and load can be mitigated by production and power generation schedule effectively and utilizing wind power can help decrease the energy costs largely. The model is also compared with the deterministic model, and some conclusions are obtained. © 2015 IEEE.

关键词： economic dispatch industrial micro-grid scenario tree wind power Wind energy Energy costs Dispatching Industrial production control LOAD

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Distributed optimization strategy for multi-region power scheduling based on alternating direction method of multipliers 12

Distributed optimization strategy for multi-region power sch...

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12th World Congress on intelligent Control and Automation, WCICA 2016

作者： Sun, Chenlin Wu, Jiang Gao, Feng Guan, Xiaohong State Key Laboratory for Manufacturing System Engineering of xi'An Jiaotong University Xi'an710049 China Ministry of Education Key Lab for Intelligent Networks and Networks Security of xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781467384148

The large-scale and frequent cross-regional electricity allocations bring about the critical need to coordinate multi-region power scheduling. It is recognized that each sub-systems' taking charge of own scheduling makes the ideal centralized optimization method infeasible. In this paper, a distributed optimization strategy based on alternating direction method of multipliers (ADMM) is put forward to solve this problem. The strategy can achieve optimal solution of global power system as the ideal centralized optimization method does, by adding steps of information exchange and iterations of decision variables to subsystems' independent scheduling. The distributed bid cost minimization model with a price-wise constant bidding curve is built to describe the multi-region scheduling problem as precisely as possible. To suit the distributed model, the standard ADMM is improved, by expanding the size of sub-systems, adding inequality constraints, and considering the complex coupling relationship between sub-systems. This coordinating strategy uses the framework of augmented Lagrangian to decompose the scheduling problem into a distributed scheduling model for regional power grid, achieving global optimal solution by updating decision variables of each sub-system and Lagrange multipliers with mapping of sub-system information. Simulations on IEEE 30 system are conducted to show the computational performance and solution quality of the proposed method. © 2016 IEEE.

关键词： Scheduling

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