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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Jingyun Cai, Yi School of Software Engineering South China University of Technology Guangzhou510650 China The Key Laboratory of Big Data and Intelligent Robots South China University of Technology and the Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9783031306778

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Contrastive Learning Cross-domain Named Entity Recognition Representation

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Non-volatile Electrically Reconfigurable Optical MZI Integrated with Sb2Se3Sub-cell Arrays for High Endurance Switching 49

Non-volatile Electrically Reconfigurable Optical MZI Integra...

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49th European Conference on Optical Communications, ECOC 2023

作者： Yang, Xing Lu, Liangjun Li, Yu Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

We demonstrated an electrical-driven non-volatile MZI based on low-loss Sb2Se3 sub-cell arrays. By constraining the material reflow in the sub-cells, optical transmission contrast of > 5 dB was achieved with > 1... 详细信息

ISBN: (纸本)9781839539954

关键词： Light transmission

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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

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Journal of Systems Science & Complexity 2023年第1期36卷 433-456页

作者： ELSAWAH A M VISHWAKARMA G K MOHAMED H S FANG Kai-Tai Department of Statistics and Data Science Faculty of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 44519Egypt Department of Mathematics&Computing Indian Institute of Technology DhanbadDhanbad 826004India College of Transportation and Civil Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China The Key Lab of Random Complex Structures and Data Analysis The Chinese Academy of SciencesBeijing 100190China

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are *** large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not *** paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation *** justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level *** results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

关键词： Aberration adjusted Gray map technique Gray map technique Hamming distance moment aberration threshold accepting algorithm uniform design

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Event-triggered H∞ Consensus of Leader-follower Multi-agent Systems 43

Event-triggered H∞ Consensus of Leader-follower Multi-agent...

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43rd Chinese Control Conference, CCC 2024

作者： Gong, Cheng Ai, Chenguang Zuo, Xuemin Ma, Zhuang Dai, W. Hebei Jianzhi Casting Group Ltd Pangzhuangzi Yutian County Tangshan064100 China College Tangshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Key Lab of Intelligent Motion Control System of Tangshan City Intelligence and Information Engineering Tangshan063000 China North China University of Science and Technology College of Electrical Engineering Tangshan063210 China Kernel University FullertonCA92832 United States

ISBN: (纸本)9789887581581

This paper discusses the H∞ consensus problem of leader-follower multi-agent systems. The controller for each agent is crafted to utilize comprehensive information from all connected agents, while an innovative event-triggered strategy is incorporated to minimize communication costs. Based on the Lyapunov function theory, an H∞ controller is proposed that can ensure both system stability and a prescribed disturbance attenuation level γ. Finally, a numerical example validated the effectiveness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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LRCu: A High-Performance Parallel Program for Identifying Hub Genes 17

LRCu: A High-Performance Parallel Program for Identifying Hu...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Liu, Jiaxin Ji, Xinglai Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing210003 China Yunnan University State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Kunming650091 China

ISBN: (纸本)9798331507398

The LeaderRank algorithm identifies influential nodes using the idea of the random walk and performs well in the identification of hub genes in biological networks. The rapid development of biological technologies promotes the evolution of networks to be highly complex and increasing largely in size. In this study, we introduce LRCu, for computing LeaderRank using CUDA, to identify hub genes. The results show that the parallel implementation is highly effective and achieves a speedup of more than 42 times for a 13,000-gene network, thereby enhancing large-scale network computation. © 2024 IEEE.

关键词： CUDA gene co-expression network hub gene parallel computing

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iNet:visual analysis of irregular transition in multivariate dynamic networks

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Frontiers of Computer Science 2022年第2期16卷 124-139页

作者： Dongming HAN Jiacheng PAN Rusheng PAN Dawei ZHOU Nan CAO Jingrui HE Mingliang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Arizona State UniversityArizona 85287America Tong Ji Intelligent Big Data Visualisation Lab(iDVx Lab) TongJi UniversityShanghai 200082China Department of Computer Science and Technology Zhengzhou UniversityZhengzhou 450001China

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks though it is not well *** this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic *** conclude features of rare categories and two types of anomalies of rare *** we present a novel rare category detection method,called DIRAD,to detect rare category candidates with *** develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex ***,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.

关键词： multivariate dynamic networks rare categories anomaly detection visual analysis

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Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch

Semi-Supervised Learning via Weight-aware Distillation under...

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International Conference on Computer Vision (ICCV)

作者： Pan Du Suyun Zhao Zisen Sheng Cuiping Li Hong Chen Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Renmin University of China Beijing China

Semi-Supervised Learning (SSL) under class distribution mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown categories unseen in the labeled ones. In such mismatch scenarios, traditional SSL suffers severe performance damage due to the harmful invasion of the instances with unknown categories into the target classifier. In this study, by strict mathematical reasoning, we reveal that the SSL error under class distribution mismatch is composed of pseudo-labeling error and invasion error, both of which jointly bound the SSL population risk. To alleviate the SSL error, we propose a robust SSL framework called Weight-Aware Distillation (WAD) that, by weights, selectively transfers knowledge beneficial to the target task from unsupervised contrastive representation to the target classifier. Specifically, WAD captures adaptive weights and high-quality pseudo-labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories. Theoretically, we prove that WAD has a tight upper bound of population risk under class distribution mismatch. Experimentally, extensive results demonstrate that WAD outperforms five state-of-the-art SSL approaches and one standard baseline on two benchmark datasets, CIFAR10 and CIFAR100, and an artificial cross-dataset. The code is available at https://***/RUC-DWBI-ML/research/tree/main/WAD-master.

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Event-Triggered single-network Control for Nonlinear Continuous-Time Zero-Sum Games with Partially Unknown Dynamics 1

Event-Triggered single-network Control for Nonlinear Continu...

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1st International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Peng, Binbin Cui, Xiaohong Wan, Zimeng Yu, Haijiao China Jiliang University The College of Mechanical and Electrical Engineering Hangzhou China China Jiliang University Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province Hangzhou China

ISBN: (纸本)9781665452151

This paper proposes an event-triggered integral reinforcement learning algorithm to solve the Hamilton-JacobiIsaacs (HJI) equation related to the zero-sum game problem. Then, we design a triggering rule to constrain the control updates. Secondly, the HJI partially differential equation is approximately solved using a single-critic neural network structure. The control law and disturbance law are modified when an event occurs. The system state and the network weights error are uniformly ultimately bounded (UUB) by Lyapunov theory. Finally, an engineering example is utilized to confirm the performance of the algorithm. © 2023 IEEE.

关键词： Reinforcement learning

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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