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作者： Pan, Zhigeng Ren, Hongyi Liu, Chang Chen, Ming Mukherjee, Mithun Yang, Wenzhen School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Center for Space-based Computing Systems Zhejiang Laboratory Hangzhou China College of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Sci-Tech University Hangzhou China Center for Intelligent Manufacturing Computing Zhejiang Laboratory Hangzhou China

Within the field of human–computer interaction, data gloves play an essential role in establishing a connection between virtual and physical environments for the realization of digital human. To enhance the credibility of human-virtual hand interactions, we aim to develop a system incorporating a data glove-embedded technology. Our proposed system collects a wide range of information (temperature, bending, and pressure of fingers) that arise during natural interactions and afterwards reproduce them within the virtual environment. Furthermore, we implement a novel traversal polling technique to facilitate the streamlined aggregation of multi-channel sensors. This mitigates the hardware complexity of the embedded system. The experimental results indicate that the data glove demonstrates a high degree of precision in acquiring real-time hand interaction information, as well as effectively displaying hand posture in real-time using Unity3D. The data glove's lightweight and compact design facilitates its versatile utilization in virtual reality interactions. © 2024 John Wiley & Sons Ltd.

关键词： Virtual reality

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Path Planning for intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1179-1189页

作者： Lan Jiang Hongyun Huang Zuohua Ding the Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China the Center of Multi-Media Big Data of Library Zhejiang Sci-Tech UniversityHangzhou 310018China

Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience *** knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

关键词： Deep Q-learning(DQL) experience replay(ER) heuristic knowledge(HK) path planning

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Multi-Instance Active Learning for Relation Extraction

Multi-Instance Active Learning for Relation Extraction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Meilin Cao Jiqiong Jiang Jing Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Research Center of Intelligent Computing Software Zhejiang Lab Hangzhou China

Achieving high-accuracy relation extraction usually requires a large well-annotated corpus for model training, which is extremely time-consuming and labor-intensive. Consequently, distant supervision, implemented as a multi-instance learning paradigm, was proposed to build models from bags using automated labeled corpora, where massive noises are unavoidably introduced in the bag labels, deteriorating the model performance. To deal with the above problem, this paper proposes novel multi-instance active learning for relation extraction, which aims to improve the classification performance of the learned models under the premise of using as few labeled training samples as possible. The proposed active learning strategies for bag selection are based on the similarity between bags, the improved Fisher matrix, and the combination of the two (hybrid), respectively. Extensive experiment results on two real-world datasets under three base learners consistently show that our proposed active learning methods archive better performance for relation extraction, compared with three baseline methods. Furthermore, the hybrid bag selection strategy performs the best.

关键词： Training Learning systems

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Learning from Crowds with Mutual Correction-Based Co-Training

Learning from Crowds with Mutual Correction-Based Co-Trainin...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Nianshang Tian Ming Wu Jiqiong Jiang Jing Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Zhejiang Lab Research Center of Intelligent Computing Software Hangzhou China

Collecting labels from crowds is a feasible solution for large knowledge graph creation. However, errors in crowd-sourced labels damage the predictive model training. Although various methods have been developed to correct noisy labels, these traditional methods on the one hand do not take full advantage of the information from crowdsourced labels, and on the other hand do not perform well facing complicated annotation conditions, especially when some crowd workers make highly correlated mistakes. To address this issue, this paper proposes a novel learning from crowds method, which can simultaneously correct label noises and learn predictive models. The proposed method trains two data classifiers and a crowd aggregator. Each data classifier divides crowdsourced labeled data into a clean set and a noisy set and predicts the labels of the instances with a large loss in the noisy set. These instances are removed from the noisy set and put into the clean set. Then, the loss of the classifier is updated using the new clean and noisy sets. The data classifier with smaller loss is selected and trained with the crowd aggregator by maximizing their mutual information. Simultaneously, the smaller loss is back propagated to the other data classifier for training it. Experimental results on both synthetic and real-world datasets consistently show that the proposed method significantly outperforms the state-of-the-art methods in noise correction and predictive model learning.

关键词： Training Crowdsourcing Annotations Predictive models Propagation losses Data models Noise measurement

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chaonong Xu Weiming Kong Min Liu Mingming Zhang Chao Li Luqi Gong Beijing Key Lab of Petroleum Data Mining China University of Petroleum (Beijing) Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× −3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy.

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Privacy-Aware UAV Flights through Self-Configuring Motion Planning

Privacy-Aware UAV Flights through Self-Configuring Motion Pl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yixing Luo Yijun Yu Zhi Jin Yao Li Zuohua Ding Yuan Zhou Yang Liu Key Lab. of High Confidence of Software Technologies(MoE) Peking University Beijing China School of Computing and Communications The Open University Milton Keynes UK School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

During flights, an unmanned aerial vehicle (UAV) may not be allowed to move across certain areas due to soft constraints such as privacy restrictions. Current methods on self-adaption focus mostly on motion planning such that the trajectory does not trespass predetermined restricted areas. When the environment is cluttered with uncertain obstacles, however, these motion planning algorithms are not flexible enough to find a trajectory that satisfies additional privacy-preserving requirements within a tight time budget during the flights. In this paper, we propose a privacy risk aware motion planning method through the reconfiguration of privacy-sensitive sensors. It minimises environmental impact by re-configuring the sensor during flight, while still guaranteeing the safety and energy hard constraints such as collision avoidance and timeliness. First, we formulate a model for assessing privacy risks of dynamically detected restricted areas. In case the UAV cannot find a feasible solution to satisfy both hard and soft constraints from the current configuration, our decision making method can then produce an optimal reconfiguration of the privacy-sensitive sensor with a more efficient trajectory. We evaluate the proposal through various simulations with different settings in a virtual environment and also validate the approach through real test flights on DJI Matrice 100 UAV.

关键词： Privacy Planning Cameras Sensors Trajectory Safety Unmanned aerial vehicles

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Modeling Self-Adaptive software Systems with Learning Petri Nets

Modeling Self-Adaptive Software Systems with Learning Petri ...

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作者： Ding, Zuohua Zhou, Yuan Zhou, Mengchu Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou310018 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NewarkNJ07102 United States

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) it can model a runtime environment;2) the components in the model can collaborate to make adaption decisions while the system is running;and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system. © 2015 IEEE.

关键词： Petri nets

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A polynomial algorithm to performance analysis of concurrent systems via petri nets and ordinary differential equations

A polynomial algorithm to performance analysis of concurrent...

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作者： Ding, Zuohua Zhou, Yuan Zhou, Meng Chu Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou310018 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102-1982 United States

In this paper, a new method is proposed to evaluate the performance of concurrent systems. A concurrent system consisting of multiple processes that communicate via message passing mechanisms is modeled by a Petri net, which is in turn represented by a set of ordinary differential equations (ODEs) of a restricted type. The equations describe the system state changes, and the solutions, also called state measures, can be used for the performance analysis such as estimating response time, throughput and efficiency. This method can avoid a state explosion problem encountered by the conventional methods based on Continuous-Time Markov Chains. Its application to an IBM business system is given as an example. © 2004-2012 IEEE.

关键词： Ordinary differential equations

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Formal modeling and verification of secure mobile agent systems

Formal modeling and verification of secure mobile agent syst...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Mingyue Jiang Zuohua Ding Mengchu Zhou Yuan Zhou Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Security of a Mobile Agent System (MAS), which has been a barrier to wider applications of a MAS, remains a compelling problem. Formal methods have been applied to this problem, and various formal models have recently been studied. However, many of them either lack of mechanisms that can protect a MAS or depend on simulation based analysis. In this paper, we concentrate on one specific category of security threats, namely, the agent-platform related security issues, and present a new method for formally modeling and verifying a secure MAS. To model a secure MAS, we equip the MAS with secure mechanisms, and propose an extended logical agent model (ELAM) for formally describing it. By transforming the ELAM to the input model of model checker SPIN, the MAS is further verified by using model checking technique. We apply ELAM to the competing airline carriers system to check its security, and our experimental results show that the proposed method works well in modeling and verifying a secure MAS.

关键词： Mobile agents Cryptography Connectors Servers Atmospheric modeling Tin

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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