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The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

Wuhan University Journal of Natural sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

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Efficient Routing Protection Algorithm Based on Optimized Network Topology

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computers, Materials & Continua 2022年第9期72卷 4525-4540页

作者： Haijun Geng Zikun Jin Jiangyuan Yao Han Zhang Zhiguo Hu Bo Yang Yingije Guo Wei Wang Qidong Zhang Guoao Duan School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Automation and Software Engineering Shanxi UniversityShanxi030006China School of Computer Science and Technology Hainan UniversityHaikou570228China Department of Computer Science&Technology Tsinghua UniversityBeijing100084China Department of Urban&Regional Planning San JoséState University San JoséCA95192USA Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai201804China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China Beijinng University of Technology Beijing-Dublin International CollegeBeijing University of TechnologyBeijing100124China

Network failures are unavoidable and occur *** the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence *** this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be *** usually employs routing protection algorithms to improve intra-domain routing ***,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in *** address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source ***,the backup path with the minimum path coincidence with the shortest path is described as an integer programming *** the simulated annealing algorithm ERPBONT is used to find the optimal ***,the algorithm is tested on the simulated topology and the real *** experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

关键词： Routing protection algorithm network availability network failure shortest path backup path

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Air-Ground Integrated Online Federated Learning Under Unreliable Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Jing, Yuqian Qu, Yuben Wu, Tao Dong, Chao Guo, Song Wu, Qihui Ministry of Industry and Information Technology Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong National University of Defense Technology Anhui Hefei230009 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

In recent years, unmanned aerial vehicles (UAVs) equipped with intelligent computing modules for machine learning (ML) have garnered attention. Federated learning (FL), as an emerging distributed learning paradigm, aligns seamlessly with this scenario. UAVs train local models while a terrestrial base station (BS) aggregates the global model, forming an air-ground integrated federated learning (AGIFL) system. However, under air-ground integrated networks with unreliable wireless links, the online retraining of ML models with new samples is promising but challenging in AGIFL. To this end, we study how to enhance the performance of air-ground integrated online FL (AGIOFL) considering packet transmission error. Specifically, we formulate a joint optimization problem for sample selection and client scheduling, aiming to minimize the loss function that maximizes the performance of AGIOFL. To solve the aforementioned problem without explicit expression, we first derive a closed-form expression for the expected convergence rate of the FL algorithm to quantify the impact of AGIOFL and unreliable wireless transmission. Based on this relationship, we reformulate the optimization problem into a mixed-integer nonlinear programming problem. Subsequently, we develop an alternating iterative optimization algorithm (OFL-PE) combining branch-and-bound with the Dinkelbach algorithm to solve this problem. Simulations indicate that OFL-PE converges within 10 iterations only, with a maximum error of less than 7% compared to the exhaustive brute force approach. In addition, we construct a proof-of-concept system based on widely used UAV embedded computers, and experimental results show that OFL-PE demonstrates superior accuracy, convergence, and robustness compared to three existing FL algorithms. © 2015 IEEE.

关键词： Federated learning

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performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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science China(information sciences) 2020年第3期63卷 288-292页

作者： Wei LI Linfeng LIAO Dawu GU Shan CAO Yixin WU Jiayao LI Zhihong ZHOU Zheng GUO Ya LIU Zhiqiang LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrate Administration Technologies for Information Security School of Microelectronics Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The Midori lightweight cipher can be applied in the RFID tags and other low-resource devices to protect information on the Internet. This cipher was proposed by Banik et al. and presented at the ASIACRYPT conference [1]. It has a Substitution Permutation Network structure with a 128-bit keysize, and two block-size variants, 64 bits

关键词： CFA Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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FIRE: a dataset for feedback integration and refinement evaluation of multimodal models 24

FIRE: a dataset for feedback integration and refinement eval...

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

作者： Pengxiang Li Zhi Gao Bofei Zhang Tao Yuan Yuwei Wu Mehrtash Harandi Yunde Jia Song-Chun Zhu Qing Li Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology and State Key Laboratory of General Artificial Intelligence BIGAI State Key Laboratory of General Artificial Intelligence BIGAI and State Key Laboratory of General Artificial Intelligence Peking University State Key Laboratory of General Artificial Intelligence BIGAI Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology and Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Department of Electrical and Computer System Engineering Monash University Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University and Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology State Key Laboratory of General Artificial Intelligence BIGAI and State Key Laboratory of General Artificial Intelligence Peking University and Department of Automation Tsinghua University

ISBN: (纸本)9798331314385

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset.

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System

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Procedia computer science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud computing Diagnosis Digital Health Edge computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

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IEEE Transactions on Signal Processing 2025年

作者： Wang, Zhe Zhang, Jiayi Bjornson, Emil Niyato, Dusit Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China KTH Royal Institute of Technology Department of Computer Science Stockholm114 28 Sweden Nanyang Technological University College of Computing & Data Science 639798 Singapore

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading *** provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination. © 2025 IEEE. All rights reserved.

关键词： Fading channels

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Active legibility in multiagent reinforcement learning

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Artificial Intelligence 2025年 346卷

作者： Liu, Yanyu Pan, Yinghui Zeng, Yifeng Ma, Biyang Doshi, Prashant School of Automation Central South University No. 605 South Lushan Road Hunan Changsha410083 China School of Artificial Intelligence & National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom School of Computer Science Minnan Normal University Fujian Zhangzhou China Department of Computer Science University of Georgia AthensGA United States

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms. © 2025 The Author(s)

关键词： Urban transportation

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Combining Perception Considerations with Artificial Intelligence in Maritime Threat Detection Systems 17

Combining Perception Considerations with Artificial Intellig...

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17th Annual System of Systems engineering Conference, SOSE 2022

作者： Chen, Ian Huang, Lucy Qiao, Jack Tamir, Dan E. Rishe, Naphtali Plano West Senior High School PlanoTX United States Glendale High School SpringfieldMO United States University of Texas at Austin Walker Department of Mechanical Engineering AustinTX United States Texas State University Department of Computer Science San MarcosTX United States Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL United States

ISBN: (纸本)9781665496230

Over the past few years the need for early-warning maritime threat detection systems has dramatically increased. Our research aims to address this need by tackling three main problems: 1) classify boat activities into three categories: random walk, following, and chasing, 2) real-time classification of boat path trajectories, and 3) designing a novel perception-based framework for activity detection in the maritime context. We propose the implementation of an entropy-based detection algorithm, trained using synthetic data. We assess the viability of the proposed framework based on accuracy and the number of time steps required prior to identification. The synthetic data generated has the potential to spur other research efforts in the field of maritime detection. © 2022 IEEE.

关键词： Boats

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