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arXiv 2021年

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

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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Adaptive Granulation for Efficient Classification on Noise Labeled Images

Adaptive Granulation for Efficient Classification on Noise L...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Kecan Cai Hongyun Zhang Duoqian Miao Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy.

关键词： Training Adaptation models Accuracy Computational modeling Noise Benchmark testing Noise measurement Standards Image classification Noise level

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Study and implementation of agent-based grid resource monitoring model

Study and implementation of agent-based grid resource monito...

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2nd International Conference on Information Science and Engineering, ICISE2010

作者： Ming, Liu Zhang, Junqi Wang, Yu Li, Jie Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781424480968

Grid is a promising infrastructure which enables scientists and engineers to access geographically distributed resources. Grid computing is a new technology which focuses on aggregating various kinds of resource (e.g., processor cycles, disk storage, and contents) into one computing platform. The realization of grid computing requires a resource agent to manage and monitor available resources. Based on study of past models, this paper presents a new agent-based resource monitoring model whose main feature is multi-layered monitoring architecture, which enhances the ability to monitor related resource efficiently. In the meantime, the new model has been implemented in the development of a resource monitoring module integrated within a academic grid project. © 2010 IEEE.

关键词： Grid computing

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Combining feature selection and resample to improve classification accuracy

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Journal of Computational Information systems 2012年第22期8卷 9539-9544页

作者： Liu, Jiaming Wang, Xiao Zhao, Qingce You, Mingyu Li, Guozheng The Key Laboratory of Embedded System and Service Computing Department of Control Science and Engineering Tongji University Shanghai Shanghai 201804 China

The paper provides the solution of the campion for CDMC2011, a data mining contest. The task for the data mining contest organized in conjunction with the ICONIP20II conference was to learn three predictive models (i.e. a classifier) capable of distinguishing between different classes for three separate tasks. Different combinations of data preprocess, feature selection and classifier learning methods were tried to obtain the best results. The performance of classifiers are improved by ensemble learning method. The final accuracy on test dataset shows our methods outperformed others. Copyright © 2012 Binary Information Press.

关键词： Intrusion detection

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ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

arXiv

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arXiv 2024年

作者： Zheng, Qi Yao, Zihao Zhang, Yaying The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods;2) overlooking spatial correlations across variables over time;3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Intrusion detection using mobile agent in ad-hoc networks

Intrusion detection using mobile agent in ad-hoc networks

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yan Xia Ren-Fa Li Ken-Li Li Key Laboratory of Embedded System and Computing College of Computer and Communication Hunan University Changsha China

ISBN: (纸本)0780384032

Security has become an important issue for mobile ad hoc networks especially for the more security-sensitive applications used in military and critical networks. As a positive protection, intrusion detection system has been successfully applied in wired networks. However, these correlative researches are not fit for mobile ad hoc networks. This paper puts forward a novel architecture based on mobile agent platform for superior intrusion detection in mobile ad hoc networks. Furthermore, an IDS agent selection algorithm and its security communication mechanism are proposed.

关键词： Intrusion detection Mobile agents Intelligent networks Ad hoc networks Mobile ad hoc networks Mobile communication computer networks embedded computing Cryptography Routing

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Study on stability of traffic flow for cooperative driving control

Study on stability of traffic flow for cooperative driving c...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Li Ping Wang Fuqiang Liu Key Laboratory of Embedded System and ServIce Computing supported by Ministry of Education University of Tongji Shanghai China

ISBN: (纸本)9781424435036

In the literature of traffic flow theory, the research on the effect on stability of traffic flow for cooperative driving control possesses an important significance. However, presently the the study on the problem is unsatisfactory because it is difficult to determine the impact qualitatively or quantitatively in real traffic experiment. In this paper, some efforts have been made for better understanding the effect on stability of traffic flow for cooperative driving control by investigating the stability for lattice traffic models, which are presented here by incorporating motion information of cars preceding. From linear stability analysis and direct simulations validation, we learn some properties of the effect on the stability and congestion waves by using the information of many other cars. First, cooperative driving behavior of many cars preceding can efficiently stabilize the traffic flow. Second, cooperative driving behavior of the cars nearby plays a prominent role in stability. Third, when the car number participating in cooperative driving policy exceeds a certain value, the congestion waves will disappear.

关键词： Traffic control Control systems Communication system traffic control Roads Laboratories embedded system embedded computing Stability analysis Transportation Mathematical model

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Bridge the Gap Between Network-Based Inference Method and Global Ranking Method in Personal Recommendation

Bridge the Gap Between Network-Based Inference Method and Gl...

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IEEE Conference on Industrial Electronics and Applications

作者： Xiwei Liu Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the... 详细信息

ISBN: (纸本)9781467386456

关键词： Eigenvalues and eigenfunctions Heating Bridges Inference algorithms Prediction algorithms Conferences Industrial electronics

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Hierarchical attention generative adversarial networks for cross-domain sentiment classification

arXiv

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arXiv 2019年

作者： Zhang, Yuebing Miao, Duoqian Wang, Jiaqi Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentimentdistinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Prediction Based on Clustering and Personality Questionnaire Data for IGD Risk

A Prediction Based on Clustering and Personality Questionnai...

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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Xiaoliang Gong Kun Fang Yichu Hou Bozhong Long Zongling Di Lei Cao National Key Laboratory of Embedded System and Service Computing (Ministry of Education) Tongji University Shanghai China

ISBN: (纸本)9781509040940

With the development of the Internet, a lot of people trapped in the network, especially the adolescent depending on the network game and disturbing their normal life. 579 freshmen participated in this work who were collected the personality questionnaires in the first week they came in university and their grades points average (GPA) after half year. The questionnaires were including Self-Control (SCS), Barratt impulse Inventory (BIS) and Chinese Big Five Personality (CBF). This work used multi-clustering algorithms to construct the models of predicting for Internet game disorder (IGD) risk, including FCM, K-means, and Hierarchical for training model. This is the first try to predict the risk of IGD by personality traits. The results shown the questionnaire data were well separated by different clustering algorithms into three groups who were shared the analogous personality traits which has a relationship with the behavior of IGD. But compared to the GPA of each group, the efficiency of the prediction model seems not so satisfactory. There need more efforts to optimized the model in the future.

关键词： Personality questionnaire Data IGD Risk FCM K-means Hierarchical

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