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8th International Conference on knowledge-Based intelligent Information and Engineering Systems, KES 2004

作者： Si, Jinxin Cao, Cungen Sui, Yuefei Yue, Xiaoli Xie, Nengfu Knowledge Acquisition and Sharing Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China The Graduate School of the Chinese Academy of Sciences China

ISBN: (纸本)9783540301325

More researchers recognize that it is an emergent and important issue that intelligent tutoring mechanism can be depicted, evaluated and measured on the uniform theoretical foundation which should be a highly formalized and computerized model in order to explore ITSs and advance effective and efficient cross-reference, fusion and integration among diverse intelligent tutoring models and systems. This paper proposes a novel uniform logic modeling method from an associative viewpoint and highlights the concrete formal models of three core elements (i.e. knowledge model, student model and pedagogical strategy model) in the architecture of an ITS. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Computer aided instruction

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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knowledge-Based intelligent Information and Engineering Systems 1

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丛书名： Lecture Notes in Computer Science

2003年

作者： Vasile Palade Robert J. Howlett Lakhmi Jain

ISBN: (数字)9783540452249

ISBN: (纸本)9783540408031

2.1 Text Summarization “Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks)” [3]. Basic and classical articles in text summarization appear in “Advances in automatic text summarization” [3]. A literature survey on information extraction and text summarization is given by Zechner [7]. In general, the process of automatic text summarization is divided into three stages: (1) analysis of the given text, (2) summarization of the text, (3) presentation of the summary in a suitable output form. Titles, abstracts and keywords are the most common summaries in Academic papers. Usually, the title, the abstract and the keywords are the first, second, and third parts of an Academic paper, respectively. The title usually describes the main issue discussed in the study and the abstract presents the reader a short description of the background, the study and its results. A keyword is either a single word (unigram), e.g.: ‘learning', or a collocation, which means a group of two or more words, representing an important concept, e.g.: ‘machine learning', ‘natural language processing'. Retrieving collocations from text was examined by Smadja [5] and automatic extraction of collocations was examined by Kita et al. [1].

关键词： Artificial Intelligence Computer Communication Networks Information Storage and Retrieval Computer and Information Systems Applications User Interfaces and Human Computer Interaction IT in Business

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GIS and Environmental Monitoring 1

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丛书名： Geotechnologies and the Environment

1000年

作者： Stavros Kolios Chrysostomos Stylios Andrei V. Vorobev Gulnara R. Vorobeva

ISBN: (数字)9783319530864

ISBN: (纸本)9783319530840;9783319850580

This book constitutes a notable contribution to investigate and present the capabilities of Geographic Information Systems (GIS) and their applicability and usefulness in environmental-related applications and sciences. The focus is on the design, creation, development and operation of integrated Web-based GIS applications for weather, marine and atmospheric environments, and the Earth's magnetic field. More specifically, the aim of this book is to present characteristic applications of GIS to environmental monitoring including GIS solutions for eco-mapping sea and port-related parameters, climate changes, and geomagnetic field.;In the first part of the book, the description of every application includes the user requirements, the design and development stages performed and the presentation of the final outcome, its capabilities and services. The Web-based applications are developed through different innovative approaches, such as cloud GIS and Google Apps for GIS, justifying the merit of WebGIS in the world of the environmental applications. The second part of the book provides an overview of geomagnetic field parameters and reveals the potential of using GIS for modeling and analyzing of the Earth's magnetic (geomagnetic) field and its parameters. Here, the authors present the recently introduced phenomenon called “geomagnetic pseudostorm”, which is modeled and further analyzed here with GIS technology and tools.;This book appeals to those interested in various areas where spatial information becomes of paramount relevance (e.g. social and economic research and mapping, environmental and climate research, decision support systems, public services, and especially for geomagnetic field variations and for the design of warning systems for natural disasters). It presents modern methods and approaches to visualize and analyze spatial information using innovative techniques, procedures, and tools of WebGIS technology. In this book, the readers find a valuable companion

关键词： Geographical Information Systems/Cartography Geophysics/Geodesy Environmental Science and Engineering

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An adaptive method for the recovery of missing samples from FHR time series

An adaptive method for the recovery of missing samples from ...

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IEEE International Symposium on Computer-Based Medical Systems

作者： Vangelis P. Oikonomou Jiri Spilka Chrysostomos Stylios Lenka Lhostka Laboratory of Knowledge and Intelligent Computing Department of Informatics and Telecommunication Technology TEI of Epirus 47100 Arta Greece

ISBN: (纸本)9781479910526

Missing data cause serious problem for automatic evaluation of the fetal heart rate(FHR) series. In this work we present an algorithm to surpress this problem. More specifically, an adaptive approach is proposed based on two steps. The first step concerns the reconstruction step where we obtain an estimate of the missing data using an empirical dictionary. The second step consists from the construction of the dictionary using the updated values from the first step. The above two steps are applied iteratively until convergence. The method adapts each time the dictionary and the reconstructed time series to the new information that we gain. Results on real and simulated experiments have shown the usefullness of our approach. More specifically, a comparison with cubic spline interpolation is performed and have shown that the proposed approach achieved 4 to 9dB better reconstruction ability.

关键词： method recovery samples

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Modeling complex logistics systems using soft computing methodology of Fuzzy Cognitive Maps

Modeling complex logistics systems using soft computing meth...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Chrysostomos D. Stylios George Georgoulas Knowledge and Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology Technological Educational Institute of Epirus Artas Greece

Fuzzy Cognitive Maps (FCMs) is an abstract soft computing modeling methodology that has been applied in many areas quite successfully. In this paper we discuss its modeling applicability to complex logistics systems involved in an intermodal container terminal and the way it could represent and handle the vast amount of information by an abstract point of view based on a decentralized approach, where the supervisor of the system is modeled as an FCM. We also investigate its applicability as a metamodel of the intermodal terminal in a simulation-optimization framework. Experts have a key role in developing the FCM as they describe a general operational and behavioral model of the system using concepts for the main aspects of the system, and weighted directed edges to represent causality. On the other hand, when data is available, data driven approaches have also been proposed for the development of FCM models. The FCM representation and implementation is discussed to develop a behavioral model of any complex system mainly based on a hierarchical structure, as well as its use as a metamodel of the system.

关键词： Logistics Adaptation models Containers Computational modeling Humans Mathematical model Neural networks

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Handling drifts and shifts in on-line data streams with evolving fuzzy systems

Handling drifts and shifts in on-line data streams with evol...

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作者： Lughofer, E. Angelov, P. Department of Knowledge-based Mathematical Systems Johannes Kepler University of Linz Altenbergerstrasse 69 A-4040 Linz Austria Intelligent Systems Research Laboratory School of Computing and Communications Lancaster University LA1 4WA United Kingdom

In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their structure (rule base and parameters) is not fixed and not pre-determined, but is extracted from data streams on-line and in an incremental manner. When dealing with so-called drifts and s hifts in data streams, one needs to take into account (1) automatic detection of drifts and shifts and (2) automatic reaction to the drifts and shifts. This is important to avoid interruptions in the learning process and downtrends in predictive accuracy. To address the first problem, we propose an approach based on the concept fuzzy rule age. The second problem is addressed by including gradual forgetting of (1) antecedent parts and (2) consequent parameters. The latter can be achieved by including a forgetting factor in the recursive local learning process of the parameters, whose value is automatically extracted based on the intensity of the shift/drift. For addressing the former problem, we introduce two alternative methods: one is based on the evolving density-based clustering (eClustering) used to form the antecedents in the eTS approach;the other is based on the automatic adaptation of the learning rate of the evolving vector quantization (eVQ) method used to form the antecedent in the FLEXFIS approach. The paper concludes with an empirical evaluation of the impact of the proposed approaches in (on-line) real-world data sets in which drifts and shifts occur. © 2010 Elsevier B.V. All rights reserved.

关键词： Fuzzy inference

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knowledge-Based intelligent Information and Engineering Systems 1

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丛书名： Lecture Notes in Computer Science

2003年

作者： Vasile Palade Robert J. Howlett Lakhmi Jain

ISBN: (数字)9783540452263

ISBN: (纸本)9783540408048

During recent decades we have witnessed not only the introduction of automation into the work environment but we have also seen a dramatic change in how automation has influenced the conditions of work. While some 30 years ago the addition of a computer was considered only for routine and boring tasks in support of humans, the balance has dramatically shifted to the computer being able to perform almost any task the human is willing to delegate. The very fast pace of change in processor and information technology has been the main driving force behind this development. Advances in automation and especially Artificial Intelligence (AI) have enabled the formation of a rather unique team with human and electronic members. The team is still supervised by the human with the machine as a subordinate associate or assistant, sharing responsibility, authority and autonomy over many tasks. The requirement for teaming human and machine in a highly dynamic and unpredictable task environment has led to impressive achievements in many supporting technologies. These include methods for system analysis, design and engineering and in particular for information processing, for cognitive and complex knowledge [1] engineering .

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