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12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2009

作者： Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Kexueyuan Nanlu #6 Beijing 100190 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

Intelligence Science is an interdisciplinary subject which dedicates to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain, research on the principles and models of natural intelligence at molecular, cellular, and behavior levels. cognitive science studies human mental activities, such as perception, learning, memory, thinking, consciousness etc. Artificial intelligence attempts simulation, extension, and expansion of human intelligence using artificial methods and technologies. Researchers specialized in above three disciplines work together to explore new concepts, theories, and methodologies. If successful, it will create a brilliant future in 21st century. The paper will outline the framework of intelligence science and present its ten big challenges. Tolerance Granular Space Model (TGSM) will be discussed as one of helpful approaches. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Artificial intelligence

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Combining the missing link: An incremental topic model of document content and hyperlink

Combining the missing link: An incremental topic model of do...

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Ma, Huifang Li, Zhixin Shi, Zhongzhi Line1 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769540122

The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction. © 2010 IEEE.

关键词： Websites

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Stabilization of Uncertain Fractional Memristor Chaotic Time⁃Delay System Based on Fractional Order Sliding Mode Control

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Journal of Harbin Institute of Technology(New Series) 2020年第4期27卷 74-83页

作者： Dawei Ding Fangfang Liu Nian Wang Dong Liang School of Electronics and Information Engineering Anhui UniversityHefei 230601China Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationHefei 230601China

Based on Lyapunov theorem and sliding mode control scheme,the chaos control of fractional memristor chaotic time⁃delay system was *** order to stabilize the system,a fractional sliding mode control method for fractional time⁃delay system was *** addition,Lyapunov stability theorem was used to analyze the control scheme theoretically,which guaranteed the stability of commensurate and non⁃commensurate order systems with or without uncertainties and ***,to illustrate the feasibility of controller,the conditions for designing the controller parameters were ***,the simulation results presented the effectiveness of the designed strategy.

关键词： fractional order system time⁃delay chaos control uncertainty sliding mode control

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Optimized Multi-kernel Dictionary Learning and Its Application in Complex Industrial Processes Monitoring 41

Optimized Multi-kernel Dictionary Learning and Its Applicati...

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第41届中国控制会议

作者： Kai Zhong Zhengping Ding Donghui Pan Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information TechnologyAnhui University School of Mathematical Science Anhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The data in the chemical process is often complex in structure,and the coupling and correlation between variables are ***,it is difficult for conventional monitoring models to learn the essential characteristics of process data,which greatly affects the performance of the *** at the above problems,this paper proposes an optimized multi-kernel dictionary learning method,which is suitable for the industrial processes with strong nonlinearity and complex data ***,the synthetic kernel fused by multiple kernel functions is used to map the process data to a new high-dimensional feature space to mine the multivariate distribution characteristics of the ***,the grid search method is used to optimize the model parameters in the kernel function for better model ***,the effectiveness and practicability of the proposed method in fault detection are verified on TE benchmark platform.

关键词： Dictionary learning Multi-kernel function Chemical process monitoring

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Modeling the growth of future web1 13

Modeling the growth of future web1

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13th International World Wide Web Conference on Alternate Track, Papers and Posters, WWW Alt. 2004

作者： Zhuge, Hai Chen, Xue Li, Xiang China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)1581139128

The future Web can be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth -?adding it to the network -?to death -?removing it from the network. Through establishing and investigating two types of models for such a network, we obtain the same scale free distribution of semantic links. Simulations and comparisons validate the rationality of the proposed models.

关键词： Semantic Web

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation 2

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Zhao, Po Chen, Jie Zhang, Chaochen Ma, Ke Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Hefei230601 China

ISBN: (纸本)9798331515669

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models. © 2024 IEEE.

关键词： few-shot learning object detection synthetic aperture radar variational feature learning

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Discovering the multi-neuronal firing patterns based on a new binless spike trains measure

Discovering the multi-neuronal firing patterns based on a ne...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Lu, Hu Wei, Hui Laboratory of Cognitive Model Algorithm School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433 China

ISBN: (纸本)9783642390647

In this paper, we proposed a method which presented a new definition of different multi-step interval ISI-distance distribution of single neuronal spike trains and formed a new feature vector to represent the original spike trains. It is a binless spike train's measure method. We used spectral clustering algorithm on new multi-dimensional feature vectors to detect the multiple neuronal firing patterns. We tested this method on standard data set in machine learning, neuronal surrogate data set and in vivo multi-electrode recordings respectively. Results shown that the method proposed in this paper can effectively improve the clustering accuracy in standard data set and detect the firing patterns in neuronal spike trains. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Clustering algorithms

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Stochastic SEIR epidemic models with virus mutation and logistic growth of susceptible populations

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Mathematics and Computers in Simulation 2023年第1期212卷 289-309页

作者： Wang, Qi Xiang, Kainan Zhu, Chunhui Zou, Lang Hunan Key Laboratory for Computation and Simulation in Science and Engineering & Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education & School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City411105 China National Center for Applied Mathematics in Hunan China

This article introduces a kind of stochastic SEIR models containing virus mutation and logistic growth of susceptible populations, whose deterministic versions have a positively invariant set and globally asymptotically stable equilibrium points. For these stochastic SEIR epidemic models, we prove they have a unique global positive solution, and also obtain sufficient conditions respectively for survival and extinction of the infectious disease. Eventually, we validate our theoretical findings using numerical simulations. © 2023 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Viruses

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Chinese microblog rumor detection based on deep sequence context

Chinese microblog rumor detection based on deep sequence con...

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作者： Lin, Dazhen Ma, Ben Cao, Donglin Li, Shaozi Department of Cognitive Science Xiamen University Xiamen China Fujian Key Laboratory of Brain-inspired Computing Technique and Applications Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Rumor is one of the main problems in social media, which often shows deeply and rapidly undesirable affection on the society. Although many rumor detection models consider content features and social features, all of them are based on the word independence assumption, which lacks the sequence context. Thus, if we use some words that often appear in rumors, our posts will be recognized as a rumor. To solve this problem, we propose a deep sequence context model (DSCM) for Chinese microblog rumor detection. This model considers two important factors of rumors: falsity and influence. Firstly, to learn falsity, we abolish the word independence assumption and use long short-term memory (LSTM) units to capture bi-direction sequence context information in content. Secondly, to learn influence, we combine the deep sequence context information with social features to learn the connection between content and social features. In our experiment, our results show that our approach outperforms several state-of-the-art machine learning approaches, including term frequency and inverse document frequency (TFIDF), LSTM, and gated recurrent unit (GRU) in rumor detection. © 2018 John Wiley & Sons, Ltd.

关键词： Long short-term memory

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