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1st International Workshop on Process-Aware systems, PAS 2014

作者： Liu, Cong Zeng, Qingtian Duan, Hua Lu, Faming The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Electronic Communication Shandong University of Science and Technology Qingdao266590 China College of Mathematics Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783662461693

Today’s workflow systems are crossing organizational boundaries and usually involve multiple organizations or partners, and the crossorganization workflow has received much public attention from both the academia and the industry. This way the property analysis as well as system control for a cross-organization workflow is very important. In this paper, we focus on a kind of loosely coupled workflow architecture with synchronous interaction pattern, i.e. each organization owns its private workflow process and can operate independently, and they need to synchronize through certain tasks. Its behavior description approach is obtained using the synchronized shuffle operation of Petri net language. It is proved that our approach benefits the behavior characterization of cross-organizational workflow with synchronous interaction pattern and can be further used to decide and control the fairness, liveness, deadlock and trap for the cross-organization workflow. A running case of cross-organizational medical diagnosis workflow is given to validate our approaches throughout the whole paper. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Petri nets

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Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity

arXiv

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arXiv 2024年

作者： Wang, Cheng Wang, Chuwen Zhang, Wang Zhao, Yu Zeng, Shirong Ning, Ronghui Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional approaches in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. Throughout the history of researching scientific problems, scientists have continuously formed new paradigms facilitated by advances in data, algorithms, and computational power. To better tackle unresolved problems, especially those of organised complexity, a novel paradigm is necessitated. While recognising that the strengths of new paradigms have expanded the scope of resolvable scientific problems, we aware that the continued advancement of data, algorithms, and computational power alone is hardly to bring a new paradigm. We posit that the integration of paradigms, which capitalises on the strengths of each, represents a promising approach. Specifically, we focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it. SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours. NGS extends beyond the capabilities of traditional mathematical modelling simulations and agent-based modelling simulations, and therefore, positions itself as a potential solution to problems of organised complexity in complex systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Adaptive social network construction using Gaussian mixture model

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Information Technology Journal 2011年第12期10卷 2252-2259页

作者： Guo, Xin Xiang, Yang Chen, Qian Wei, Wei Department of Computer Science and Technology School of Electronic and Information Engineering Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

A Social network graph shows social interactions and relationships between individuals in a specific social environment, which is very helpful for analyzing social relationships, activities, structures, etc. The author quantized the strengths of social objects' relationships in social environment using an improved vector space model. Gaussian mixture model was employed to set the threshold for identifying social relationships adaptively and divide social subgroups automatically. According to the threshold, social network graph would be constructed based on performance measures. It is concluded that hidden social relationships can be discovered effectively by using this approach which is very flexible and adaptive for dynamic information feedback mechanism. © 2011 Asian Network for Scientific Information.

关键词： Gaussian distribution

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Implicit privacy preservation:a framework based on data generation

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Security and Safety 2022年第1期1卷 166-183页

作者： Qing Yang Cheng Wang Teng Hu Xue Chen Changjun Jiang Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

This paper addresses a special and imperceptible class of privacy,called implicit *** contrast to traditional(explicit)privacy,implicit privacy has two essential properties:(1)It is not initially de ned as a privacy attribute;(2)it is strongly associated with privacy *** other words,attackers could utilize it to infer privacy attributes with a certain probability,indirectly resulting in the disclosure of private *** deal with the implicit privacy disclosure problem,we give a measurable de nition of implicit privacy,and propose an ex-ante implicit privacy-preserving framework based on data generation,called *** framework consists of an implicit privacy detection module and an implicit privacy protection *** former uses normalized mutual information to detect implicit privacy attributes that are strongly related to traditional privacy *** on the idea of data generation,the latter equips the Generative Adversarial Network(GAN)framework with an additional discriminator,which is used to eliminate the association between traditional privacy attributes and implicit *** elaborate a theoretical analysis for the convergence of the *** demonstrate that with the learned generator,IMPOSTER can alleviate the disclosure of implicit privacy while maintaining good data utility.

关键词： Privacy preservation Implicit privacy Generative adversarial network Data utility Data generation

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StressBar: A system for stress information collection 11

StressBar: A system for stress information collection

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2nd Wireless Health Conference, WH'11

作者： Lu, Dajun Zhan, Guoxing Wang, Shinan Shi, Weisong Wiholm, Clairy Arnetz, Bengt B. Dept. of Computer Science Wayne State University United States Dept. of Public Health and Caring Sciences Uppsala University Sweden Dept. of Family Medicine and Public Health Sciences Wayne State University Detroit MI United States Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781450309820

The causes of stress and how it affects our behaviors are generally not well understood. The stress research usually requires a large amount of data to analyze possible stress-related factors. The data collection process traditionally is time-consuming and cost-ineffective. To help medical researchers collect the stress information, we propose a tool named StressBar utilizing the powerful data collection capacity of smart phones. In this paper, we will show our design and implementation considerations of StressBar. Copyright 2011 ACM.

关键词： Smartphones

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Learning Topic Emotion and Logical Semantic for Video Paragraph Captioning

SSRN

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SSRN 2024年

作者： Li, Qinyu Wang, Hanli Yi, Xiaokai Department of Computer Science & Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai200092 China

Video paragraph captioning aims to generate multiple descriptive sentences for videos, which strive to replicate human writing in accuracy, logicality, and richness. However, current research focuses on the accuracy and temporal order of events, ignoring emotion and other critical logical relations embedded in human language, such as causal and adversative relations. The ignorance impairs the reasonable transition across generated event descriptions and restricts the vividness of expression, resulting in a gap from the standard of human language. To resolve the problem, a framework that integrates logic and emotion representation learning is proposed to narrow the gap. Concretely, a large-scale inter-event relation corpus is constructed based on the EMVPC dataset. This corpus is named EMVPC-EvtRel (standing for "EMVPC-Event Relations") and contains six widely-used logical relations in human writing, 127 explicit inter-sentence connectives, and over 20,000 pairs of event segments with newly annotated logical relations. A logical semantic representation learning method is developed for recognizing the dependencies between visual events, thereby enhancing the characteristics of video contents and boosting the logicality of generated paragraphs. Moreover, a fine-grained emotion recognition module is designed to uncover emotion features embedded in videos. Finally, experimental results on the EMVPC dataset demonstrate the superiority of the proposed method compared to existing state-of-the-art approaches. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Null space based discriminant sparse representation large margin for face recognition

Null space based discriminant sparse representation large ma...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ying Wen Lili Hou Lianghua He Department of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781479919611

In this paper, we propose a novel subspace learning algorithm, termed as null space based discriminant sparse representation large margin (NDSLM). There are two contributions in the paper. First, we propose a new expectation to obtain the neighborhood information for large margin subspace learning, i.e., the within-neighborhood scatter and between-neighborhood scatter are modeled by the sparse reconstruction weights of the samples from the same class and different classes, respectively. Since the neighborhood information formed by sparse representation can capture non-linearities in the data, the proposed method possesses more discriminative information than the traditional large margin learning methods with the expectation using Euclidean distance, etc. Second, the large margin information integrated into the model of Fisher criterion makes the discriminating power of NDSLM further boosted. NDSLM addresses the small sample size problem by solving an eigenvalue problem in null space. Experiments on ORL, Yale, AR, Extended Yale B and CMU PIE five face databases are performed to evaluate the proposed algorithm and the results demonstrate the effectiveness of NDSLM.

关键词： Principal component analysis Silicon Pipelines Glass Databases

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Decomposition based efficient algorithms for knowledge reduction

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ICIC Express Letters 2010年第3期4卷 1025-1032页

作者： Jiao, Na Miao, Duoqian Zhang, Hongyun Department of Computer Science and Technology Ministry of Education of China Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Knowledge reduction, one of essential issues for data mining, has alwaysbeen a, hot topic due to the explosive growth of inform,a,tion. However, whenhandling large-scale data, m,any current knowledge reduction m,ethod,s based, onrough set theory are incapable. In this paper, efficient algorithms forknowledge reduction are put forward, based, on decomposition principle. Theidea, of decomposition is to break a, complex problem, down into a,m,a,ster-problem, and, several sub-problem,s that are simpler, m,orem,a,na,gea,ble and, m,ore solvable by using existing induction methods, thenjoining them, together in order to solve the original problem,. Compared, withsom,e traditional methods, the efficiency of the proposed algorithms can beillustrated by experiments with standard datasets from, UCI database. ICIC International © 2010.

关键词： Rough set theory

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DCOM-GNN: A Deep Clustering Optimization Method for Graph

SSRN

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SSRN 2023年

作者： Yang, Haoran Wang, Junli Duan, Rui Yan, Chungang Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Deep clustering plays an important role in data analysis, and with the prevalence of graph data nowadays, various deep clustering models on graph are constantly proposed. However, due to the lack of more adequate clustering guidance, the discriminability of feature representation learned from these models for the clustering task is limited. Therefore, for the purpose of enabling the output of these models to be more cluster-oriented, we propose a Deep Clustering Optimization Method for Graph Neural Networks (DCOM-GNN), which can be attached to the original model architecture conveniently. For DCOM-GNN, it contains two components, one is the inter-cluster distance optimization module, whose role is to further adjust the distance between clusters of the original model output rationally. Another one is the intra-cluster distance optimization module, which aims to improve the cohesiveness of the original model output. Comprehensive experiments show that the performance of various deep clustering models on graph can be significantly improved after adding DCOM-GNN. © 2023, The Authors. All rights reserved.

关键词： Cluster analysis

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks

A Multi-label Classifier for Human Protein Subcellular Local...

引用

2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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