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Nested Named Entity Recognition: A Survey of Latest research

Expert systems 2025年第7期42卷

作者： Lixia Ji Yiping Dang Yunlong Du Wenzhao Gao Han Zhang School of Cyber Science and Engineering Zhengzhou University Zhengzhou China College of Computer Science Sichuan University Chengdu China Intelligent Systems Research Centre School of Computing Engineering & Intelligent Systems Ulster University Belfast UK Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China

The research on nested named entity recognition (NER) is conducive to providing richer semantic representations and capturing the nested structure among entities, which is crucial for the execution of downstream tasks. This paper aims to summarise the nested NER methods that have been combined with emerging technologies in recent years. We summarise the nested NER methods that are integrated with emerging technologies from three dimensions: model, framework, and data. Additionally, we explore the research progress of nested NER in two scenarios: cross-lingual modality and multi-modal in different modalities. Furthermore, we discuss the practical applications of NER technology in five fields: biomedicine, justice, finance, media, and e-commerce. Through this review, we can clearly see the development trends of nested NER technology under emerging technologies and different modalities, as well as its broad application prospects in various fields. This provides a reference for future exploration directions in nested NER.

关键词： application fields modal nested named entity recognition new technologies

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Evaluation of server virtualization technologies for Green IT

Evaluation of server virtualization technologies for Green I...

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6th IEEE International Symposium on Service-Oriented System Engineering, SOSE 2011

作者： Liu, Lu Masfary, Osama Li, Jianxin Distributed and Intelligent Systems Research Group School of Computing and Mathematics University of Derby United Kingdom School of Computing Beihang University Beijing China

ISBN: (纸本)9781467304108

Service oriented computing (SOC) and cloud computing paradigm has emerged as an approach which enables ubiquitous, on-demand network accesses to a shared pool of flexibly reconfigurable computing resources. The increasing trends of electrical consumption within data centres are a growing concern for business owners as they are quickly becoming a large fraction of the total cost of ownership. There are several challenges and opportunities within a data centre to lower the electrical costs and improve the energy efficiency. Since servers and air conditioners represent the top users of electrical power in the data centre, this research sets out to explore methods from each subsystem of the data centre as part of an overall energy efficient solution. In this paper, we investigate the current trends of Green IT awareness and how the deployment of IT equipment and Site Infrastructure equipment optimization techniques which can offer a solution to a global issue by reducing carbon emissions. © 2011 IEEE.

关键词： Virtualization

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Pingjie Ou Ningjiang Chen Long Yang School of Computer and Electronic Information Guangxi University Nanning 530004 Guangxi China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning 530004 Guangxi China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning 530004 Guangxi China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data’s inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions.

关键词： Cache memory

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Towards automatic update of access control policy 24

Towards automatic update of access control policy

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24th Large Installation System Administration Conference, LISA 2010

作者： Hu, Jinwei Zhang, Yan Li, Ruixuan Intelligent Systems Laboratory School of Computing and Mathematics University of Western Sydney Sydney1797 Australia Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are subject to changes, so that the associated configurations need to be updated accordingly in order to reflect the systems' evolution. Access control update is complex, especially for large-scale systems;because the updated system is expected to meet necessary constraints. This paper presents a tool, RoleUpdater, which answers administrators' high-level update request for role-based access control systems. RoleUpdater is able to automatically check whether a required update is achievable and, if so, to construct a reference model. In light of this model, administrators could fulfill the changes to RBAC systems. RoleUpdater is able to cope with practical update requests, e.g., that include role hierarchies and administrative rules in effect. Moreover, RoleUpdater can also provide minimal update in the sense that no redundant changes are implemented. © LISA 2010.

关键词： Access control

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SOLUTION OF POWER-SYSTEM PROBLEMS THROUGH THE USE OF THE OBJECT-ORIENTED PARADIGM

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY systems 1994年第3期16卷 157-165页

作者： DILLON, TS CHANG, E [a]Expert Intelligent Systems Laboratory Applied Computing Research Institute Australia [b]Department of Computer Science Computer Engineering La Trobe University Bundoora Vic 3083 Australia

The paper presents a description of the state of the art of the application of object-oriented systems to power system problems. It presents a brief resume of the object-oriented concepts. It examines the application in three areas, namely: traditional software applications, database systems and knowledge-based systems. It also looks at all three stages of the use of object-oriented models in these three areas. The three stages are the conceptual model, the software structure model and the implementation model. It provides a set of conclusions as to future areas of applications of the object-oriented paradigm to power systems applications.

关键词： SOFTWARE ENGINEERING DATABASES KNOWLEDGE-BASED systems AND OBJECT-ORIENTED systems

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Nanoscaffold Ba_0.6Sr_0.4TiO₃:Nd₂O₃ ferroelectric memristors crossbar array for neuromorphic computing and secure encryption

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Journal of Materiomics 2025年第5期11卷

作者： Zhang, Weifeng Xu, Jikang Wang, Yongrui Zhang, Yinxing Wang, Yu Li, Pengfei Jia, Yongqing Zhao, Zhen Li, Changliang Yang, Biao Hou, Yue Guo, Zhenqiang Huang, Zeze Qi, Yincheng Yan, Xiaobing Institute of Life Science and Green Development Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province College of Electronic and Information Engineering Hebei Baoding 071002 China Hebei Key Lab of Optic-Electronic Information and Materials College of Physics Science & Technology Hebei Baoding 071002 China Department of Electronic and Communication Engineering Intelligent Vision Computing Research Institute North China Electric Power University Hebei Baoding 071003 China

Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba_0.6Sr_0.4TiO₃)_0.5(Nd₂O₃)_0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La_0.67Sr_0.33MnO₃/SrTiO₃/P[sbnd]Si substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm²), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 10⁹ switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors. © 2025 The Authors

关键词： Ferroelectric memristor Interface channel Multi-factor secure encryption system Nanoscaffolded

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Performance measurements in iVIC virtual machine clones

Performance measurements in iVIC virtual machine clones

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26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012

作者： Hardy, James Liu, Lu Bessis, Nik Cui, Lei Li, Jianxin Distributed and Intelligent Systems Research Group School of Computing and Mathematics University of Derby Derby United Kingdom Advanced Computing Technology Institute Beihang University Beijing China

ISBN: (纸本)9780769546520

Virtualisation is a prevalent technology in current computing. Virtualisation can reduce hardware costs by server consolidation, implement "green computing" by reducing power consumption and is an underpinning process for cloud computing enabling the creation of a range of virtual networks and virtual supercomputers. This paper presents performance measurements for a cloning system known as iVIC developed in Beihang University, China. iVIC creates clusters of virtual computers that can communicate with each other through virtual switch mechanisms. The virtual switches can also allow communication between the clone environment and the physical world. The paper presents the results of impartial testing to identify the limiting factors for creating and starting numbers of clone machines, measure the power consumption of the physical system and the computational performance capability of the clones. © 2012 IEEE.

关键词： Virtual machine

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Future supply chain processes and data for collective intelligence

Future supply chain processes and data for collective intell...

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5th International Conference on P2P, Parallel, Grid, Cloud and Internet computing, 3PGCIC 2010

作者： Watmough, Martin Polovina, Simon Khazaeri, Babak Hill, Richard Conceptual Structures Research Group Department of Computing Sheffield Hallam University United Kingdom Distributed and Intelligent Systems Research Group University of Derby United Kingdom

ISBN: (纸本)9780769542379

The aim of this paper is to identify the requirements on data that are necessary to support a collective intelligence solution for future supply chains. It is based on an evidential discussion of the information flows in future supply chains and the impact these will have on the life cycle, governance and ownership of data. Supply Chains are facing an increasing complex future if they are to provide the services required while operating in increasingly diverse landscapes and environments. Integration and coordination across the whole supply chain is requiring new approaches, systems and processes therefore data must be enhanced to support these new demands and provide the agility that organisations and consumers envisage. The content of data or data sets is discussed in the context of semantics and Service Orientated Architecture (SOA). A shift in the content of data to include behaviour and increased synchronisation across processes is developed as a possible solution for future supply chains. The evolution and importance of understanding is discussed and developed into a reliance on the integration of data and processes through change control and governance to achieve a collective intelligence across the supply chain. © 2010 IEEE.

关键词： Semantics

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Towards On-Line Analytical Mining in Large Databases

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SIGMOD Record (ACM Special Interest Group on Management of Data) 1998年第1期27卷 97-107页

作者： Han, Jiawei Intelligent Database Systems Research Laboratory School of Computing Science Simon Fraser University British Columbia Canada V5A 1S6

Great efforts have been paid in the intelligent Database systems research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems. Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner. This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.

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