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共 573 条记录，以下是161-170 订阅

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Synchronization of Multi-Weighted and Directed Network Under Pinning Impulsive Control

Synchronization of Multi-Weighted and Directed Network Under...

引用

2021 China Automation Congress, CAC 2021

作者： Hu, Xiaohan Liu, Xiwei Department of Computer Science and Technology Tongji University Shanghai201804 China Department of Computer Science and Technology Key Laboratory of Embedded System Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9781665426473

In this paper, multi-weighted and directed complex dynamic networks (MWDCDNs) under the control of hybrid impulses are analyzed. Two cases are considered. In the first case, impulses can be only applied on one fixed node. In the other case, impulses are applied on different nodes according to whether they are synchronized or de-synchronized in MWDCDNs with unbounded time delay. By limiting impulsive interval and Chebyshev distance between the normalized left eigenvectors (NLEVec) of multiple coupling matrices corresponding to the zero eigenvalue, we obtain some criteria for the synchronization of MWDCNs. Some numerical simulation are also given. © 2021 IEEE

关键词： Eigenvalues and eigenfunctions

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Use the core clusters for the initialization of the clustering based on one-class support vector machine

Use the core clusters for the initialization of the clusteri...

引用

4th International Conference on Advances in Swarm Intelligence, ICSI 2013

作者： Gu, Lei Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

ISBN: (纸本)9783642387142

The clustering method based on one-class support vector machine has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random datasets are employed for its initialization. In this paper, a novel initialization method based on the core clusters is used for the clustering algorithm based one-class support vector machine. The core clusters are gained by constructing the neighborhood graph and they are regarded as the initial datasets of the clustering algorithm based one-class support vector machine. To investigate the effectiveness of the proposed approach, several experiments are done on four datasets. Experimental results show that the new presented method can improve the clustering performance compared to the previous clustering algorithm based on one-class support vector machine and k-means approach. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Support vector machines

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method

Correct after Answer: Enhancing Multi-Span Question Answerin...

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

作者： Lin, Jiayi Zhang, Chenyang Tong, Haibo Zhang, Dongyu Hong, Qingqing Hou, Bingxuan Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

ISBN: (纸本)9798891761681

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the incorrect predictions. Through a statistical analysis, we observe that models with stronger abilities do not predict less incorrect predictions compared with other models. In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. Specifically, the ACC framework first introduces a classifier to classify the predictions into three types and exclude "wrong predictions", then introduces a corrector to modify "partially correct predictions". Experiments on several MSQA datasets show that ACC framework significantly improves the Exact Match (EM) scores, and further analysis demostrates that ACC framework efficiently reduces the number of incorrect predictions, improving the quality of predictions. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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A remote debugging system for OpenMP parallel program

A remote debugging system for OpenMP parallel program

引用

2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Chai, Hua Shi, Qinqin Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 201418 China

ISBN: (纸本)9781457717017

A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use. © 2011 IEEE.

关键词： User interfaces

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An evolutionary semi-supervised subtractive clustering method by seeding

An evolutionary semi-supervised subtractive clustering metho...

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2012 International Conference on Information Technology and Software Engineering, ITSE 2012

ISBN: (纸本)9783642345210

A semi-supervised subtractive clustering has been proposed recently. However, it performance depends greatly on the choice of the parameters of the mountain function and only proper parameters enable the clustering method to produce a better effect. In this paper, an evolutionary semi-supervised subtractive clustering method by seeding is presented. The novel approach uses the genetic algorithms for tuning the relative parameters of the subtractive clustering by minimizing the Davies Bouldin clustering validity index regarded as the fitness function. To investigate the effectiveness of our approach, several experiments are done three real datasets. Experimental results show that our proposed method can improve the clustering performance significantly compared to other two traditional semi-supervised clustering algorithms. © 2013 Springer-Verlag.

关键词： Genetic algorithms

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ADHD Discrimination Based on Social Network

ADHD Discrimination Based on Social Network

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International Conference on Cloud computing and Big Data (CCBD)

作者： Xiaojiao Guo Lianghua He The key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479966226

Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms and normal by the existing research. In this paper, the contributions are as two aspects: one is that the attributes of brain network of the resting state fMRI data have been calculated to discriminate three categories ADHD from the controls. And the average accuracies of various categories are 42.49% and 63.46% on the ADHD-200 datasets of NYU and KKI respectively, which is better than the average best imaging-based diagnostic performance of 35.19% and 61.90% achieved in the ADHD-200 global competition. The other one is that we put forward a new method named G-algorithm to construct the whole brain network, which based on certain rules. The same or even better classification results have been achieved by this method, which also verifies its feasibility and effectiveness.

关键词： Social network services Accuracy Support vector machines Correlation Synchronization Diseases Organizations

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Entropy-based Traffic Congestion Propagation Pattern Mining with GPS Data

Entropy-based Traffic Congestion Propagation Pattern Mining ...

引用

IEEE International Conference on Big Data Analysis (ICBDA)

作者： Xinyuan Sui Yaying Zhang Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Traffic congestion in urban areas has become a critical issue in the world. As the limits of road topology and the influence of residents' commuting periodic patterns, the same part of the road network usually gets congested, and there are similar propagation paths when congestion occurs in an area. This paper aims to model the congestion propagation phenomenon and further discover the congestion propagation paths that frequently appeared. To this end, we propose an algorithm which analyzes the correlations between congestions on different road segments and constructs a congestion propagation graph to reflect the dynamic process of congestions. The traffic flow entropy is novelty considered to identify the potential essential road segment in traffic congestions. We adopt the Jaccard correlation to estimate the parameters employed. The experiments with the road network in Beijing, China and real-world GPS trajectory data indicate that our approach can reveal congestion propagation patterns and the dynamic process of congestion propagation.

关键词： Correlation Network topology Roads Heuristic algorithms Urban areas Trajectory Topology

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Local-directed network coding in vehicular ad-hoc networks

Local-directed network coding in vehicular ad-hoc networks

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IEEE International Symposium on Intelligent Signal Processing and Communication systems (ISPACS)

作者： Haojie Luo Dongdong Zhang Chen Ye The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

In this paper, we proposed a novel network coding method called local-directed network coding to improve the throughput of the vehicular ad-hoc network. Different from other vehicular network coding methods, we consider the direction of packet transmission. In detail, the intermediate node selects one main packet and some sub-packets from forwards and backwards sending queues, and then encodes these packets into one packet to broadcast. Simulation results show that our approach can efficiently improve the throughput of application layer in vehicular networks.

关键词： Encoding Buffer storage Accidents Vehicles Streaming media Artificial neural networks Performance evaluation

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Counting Pedestrian in Crowded Subway Scene

Counting Pedestrian in Crowded Subway Scene

引用

Congress on Image and Signal Processing, CISP

作者： Keju Zu Fuqiang Liu Zhipeng Li Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

When the high occlusion occurs in crowded scene, face detection is a better substitute for detecting pedestrian. In this paper, we present a novel crowd analysis method based on discriminative descriptor of faces and support vector machine (SVM) ensemble. Through manipulating the input features in the same sample set, the different input features of faces are extracted to train two SVM classifiers. The classification scores of two generated classifiers are combined adaptively to make a collective decision. The first SVM, as the principal classifier gives out most of face hypotheses, while the second SVM serves as secondary one to rejecting the false positive. We present experiment to test the proposed method in crowded subway video, and the result shows that the SVM ensemble outperforms the single SVM in counting the pedestrian.

关键词： Layout Support vector machines Support vector machine classification Face detection Histograms Surveillance Humans Lighting Cameras Feature extraction

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Image Retrieval Based on Manifold Learning and Incorporate Clustering

Image Retrieval Based on Manifold Learning and Incorporate C...

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GCIS, WRI Global Congress on Intelligent systems

作者： Jianzhu Cui Fuqiang Liu Zhipeng Li Jing Li The Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

This paper presents a novel unsupervised learning framework named image retrieval based on manifold learning and incorporate clustering. The dimensionality of image descriptors used in image retrieval applications is quite high. Given a query image, our algorithm first makes use of manifold learning (LPP) for dimensionality reduction and manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly. Then we use the similarities among target images for improving the performance of the image retrieval systems by cluster-based retrieval of images by unsupervised learning. Our algorithm retrieves image clusters as retrieval results by applying K-means clustering algorithm to a collection of images collected by manifold ranking algorithm. Experimental results on a general-purpose image database show that our algorithm attains a significant improvement over existing systems.

关键词： Image retrieval Clustering algorithms Feedback Unsupervised learning Support vector machines Support vector machine classification Image databases Content based retrieval Intelligent systems laboratories

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