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arXiv 2023年

作者： Zhang, Xiao Zhang, Hai Zhou, Hongtu Huang, Chang Zhang, Di Ye, Chen Zhao, Junqiao The Department of Computer Science and Technology Tongji University China The MOE Key Lab of Embedded System and Service Computing Tongji University China

Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend to adopt overly conservative policy to avoid unsafe situations. However, overly conservative policy severely hinders the exploration, and makes the algorithms substantially less rewarding. In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states. The boundary we construct is equivalent to distinguishing dead-end states, indicating the maximum extent to which safe exploration is guaranteed, and thus has minimum limitation on exploration. Similar to Recovery Reinforcement Learning, we utilize a decoupled RL framework to learn two policies, (1) a task policy that only considers improving the task performance, and (2) a recovery policy that maximizes safety. The recovery policy and a corresponding safety critic are pretrained on an offline dataset, in which the safety critic evaluates upper bound of safety in each state as awareness of environmental safety for the agent. During online training, a behavior correction mechanism is adopted, ensuring the agent to interact with the environment using safe actions only. Finally, experiments of continuous control tasks demonstrate that our approach has better task performance with less safety violations than state-of-the-art algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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DCOM-GNN: A Deep Clustering Optimization Method for Graph

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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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An ultrasound system for tumor detection in soft tissues using low transient pulse

An ultrasound system for tumor detection in soft tissues usi...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Ashish R. Ratnakar MengChu Zhou Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

This work presents a method to detect the size and location of tumor in soft tissues using ultrasound. Quantitative ultrasound is utilized to allow an ultrasound signal to be sent from a transmitter to multiple receivers. This received signal is analyzed for echogenic and echolucent tumors to differentiate between the two along with non-tumor sample and also studied for the delay to determine the size/location of the tumor. The proposed system utilizes Low Transient Pulse (LTP) technique and is implemented using Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technologies. In this co-design architecture, DSP carries out the analysis of received demodulated signal at a lower speed while FPGA runs at a higher one to generate LTP signal and demodulate bandpass ultrasonic signal. This work elaborates the implementation of Quadrature Amplitude Modulation (QAM) receiver on FPGA for the received signal from an ultrasound detector. LTP is applied to the tumor samples through the transmitter and the received signal at an ultrasonic receiver is passed through QAM to obtain different maxima that are then further used to compute the location and the size of the tumor using DSP. This dual platform co-design demonstrates a good application of a FPGA/DSP platform for the LTP generation and received signal processing.

关键词： Digital signal processing Field programmable gate arrays Tumors Ultrasonic imaging Clocks Quadrature amplitude modulation Receivers

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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...

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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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Microservices Architecture Based Cloudware Deployment Platform for service computing

Microservices Architecture Based Cloudware Deployment Platfo...

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2016 IEEE Symposium on service-Oriented system Engineering (SOSE)

作者： Dong Guo Wei Wang Guosun Zeng Zerong Wei Tongji Branch National Engineering & Technology Center of High Performance Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

With the rising of Cloud computing, evolution have occurred not only in datacenter, but also in software development, deployment, maintain and usage. How to build cloud platform for traditional software, and how to deliver cloud service to users are central research fields which will have a huge impact. In recent years, the development of microservice and container technology make software paradigm evolve towards Cloudware in cloud environment. Cloudware, which is based on service and supported by cloud platform, is an important method to cloudalize traditional software. It is also a significant way for software development, deployment, maintenance and usage in future cloud environment. Furthermore, it creates a completely new thought for software in cloud platform. In this paper, we proposed a new Cloudware PaaS platform based on microservice architecture and light weighted container technology. We can directly deploy traditional software which provides services to users by browser in this platform without any modification. By utilizing the microservice architecture, this platform has the characteristics of scalability, auto-deployment, disaster recovery and elastic configuration.

关键词： Cloud computing Containers Computer architecture Rendering (computer graphics) Virtualization Servers

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Transport complexity of data dissemination in large-scale online social networks 19

Transport complexity of data dissemination in large-scale on...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Wang, Cheng Zhu, Hangyu Wang, Chaodong Zhao, Qin Zhang, Bo Key Laboratory of Embedded Systems Service Computing Ministry of Education Tongji University Shanghai China East China Branch China Academy of Information and Communications Technology Shanghai China Department of Computer Science Shanghai Normal University Tongji University Shanghai China

ISBN: (纸本)9781450371582

In this paper, we aim to measure the transport difficulty of data dissemination in online social networks (OSNs). We focus on a usual type of interest-driven social sessions in OSNs, called Social-InterestCast, under which users autonomously determine whether to view the content from their followees depending on their interest. We define the fundamental limit of transport load as a new metric, called transport complexity, i.e., the minimally required transport load for an OSN in a given carrier network. We derive the transport complexity for Social-InterestCast sessions in a large-scale OSN in the carrier network with optimal communication architecture. The results can act as the common lower bounds on transport load for Social-InterestCast in any carrier networks. To the best of our knowledge, this is the first work to measure the transport difficulty for data dissemination in OSNs by modeling session patterns with the interest-driven characteristics. © 2019 Association for computing Machinery.

关键词： Social networking (online)

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Improved condition for controllability of strongly dependent strict minimal siphons in Petri nets

Improved condition for controllability of strongly dependent...

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IEEE International Conference on Networking, Sensing and Control

作者： GuanJun Liu ChangJun Jiang MengChu Zhou Key Laboratory of the MoE for Embedded System and Service Computing University of Tongji Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Li and Zhou propose an important concept for Petri nets: elementary siphons. They partition siphons into elementary and dependent ones. The controllability of the latter can be ensured by the former's proper control. They give a sufficient condition to decide whether a dependent siphon is controlled by its elementary ones in S 3 PR. However, this condition is so loose that in many cases the controllability of a dependent SMS cannot be determined although it is actually controlled. In this paper, we propose an improved condition to decide the controllability of strongly dependent SMS.

关键词： Controllability system recovery Nickel Flexible manufacturing systems Petri nets Sufficient conditions Control theory

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Predictive Control for Visual Servo Stabilization of Nonholonomic Mobile Robots

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Acta Automatica Sinica 2013年第8期39卷 1238-1245页

作者： Zheng-Cai CAO Long-Jie YIN Yi-Li FU Tian-Long LIU College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin 150080 China

Visual servo stabilization of nonholonomic mobile robots has gained extensive attention. However, currently, the solution of the problem does not consider both the visibility constraints and the actuator limitations, so the designed controller is difficult to realize satisfactory performance in practical application. In this paper, a predictive controller for the visual servo stabilization of a mobile robot is presented. Firstly, a kinematic predictive stabilization controller utilized to generate the command of velocity is introduced. Then, in order to make the actual velocity of the mobile robot asymptotically approach to the desired one, a dynamic predictive controller is designed. The proposed predictive controller can deal with the constraints easily. Finally, several simulations are performed, and the results illustrate that the proposed control scheme is effective to solve the visual servo stabilization problem.

关键词： Nonholonomic mobile robots visual servo stabilization predictive control constraints

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Efficient Routing Strategy for Sdn Microservices Based on 5g Cloud Native Scenario

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SSRN 2022年

作者： Sheng, Shuo Yan, Chungang Key Laboratory ofEmbedded System and Service Computing Ministry of Education Tongji University Shanghai China

In the 5G cloud native scenario, a variety of heterogeneous networks are combined and decoupled based on the SDN multi-dimensional splitting method to find a way to restructure SDN within the microservice framework. In order to solve this problem, this paper proposes a microservice splitting idea within the cloud native framework system to conduct multi-dimensional restructuring of SDN, and proposes a new algorithm to improve the QOS of SDN under the microservice framework, further reducing end-to-end delay Cost, load balancing, energy consumption and other indicators, experiments prove that the SDN + micro service splitting idea in this paper is progressiveness, and multi-objective optimization is carried out based on the optimization algorithm, and the performance is significantly improved. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Graph Ensemble Neural Network

SSRN

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SSRN 2023年

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

Ensemble methods have been shown to improve graph neural networks (GNNs). Existing ensemble methods on graphs determine a strong classifier by combining a set of trained base classifiers, i.e., combining the final outputs of base classifiers for prediction. However, these methods fail to promote many popular GNNs to perform well under heterophily (in graphs where many connected nodes havedifferent class labels), which limits their applicability. Furthermore, they ignore the hierarchical nature of GNNs, which results in no interaction between base classifiers when neighbors are aggregated (during training). Two issues arise from this: low applicability and shallow ensemble. We propose Graph Ensemble Neural Network (GEN) for addressing above issues, which is not a simple ensembleof GNNs, but instead integrates ensemble into GNNs to fuse a set of graphs. GEN deepens single ensemble into multiple ensembles during training and applies to homophily and heterophily graphs. In GEN, we design structure augmentation to generate some graphs for training and design feature augmentation for attenuating errors brought by the initial features. Different from existing graphensemble methods that execute only one ensemble, GEN executes multiple deep ensembles throughout the neighbor aggregation to fuse multiple graphs generated by structure augmentation. Extensive experiments show that GEN achieves new state-of-the-art performance on homophily and heterophily graphs for the semi- and full-supervised node classification. The source code of GEN is publicly available at https://***/graphNN/GEN1. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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