Fangping Lan - Academia.edu (original) (raw)
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Papers by Fangping Lan
2022 International Conference on Computer Communications and Networks (ICCCN)
5th Asia-Pacific Workshop on Networking (APNet 2021), 2021
Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks, 2021
IEEE/CAA Journal of Automatica Sinica
Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture re... more Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications. This paper presents a novel deep learning network for hand gesture recognition. The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation. To learn short-term features, each video input is segmented into a fixed number of frame groups. A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot. These two entities are fused and fed into a convolutional neural network (ConvNet) for feature extraction. The ConvNets for all groups share parameters. To learn long-term features, outputs from all ConvNets are fed into a long short-term memory (LSTM) network, by which a final classification result is predicted. The new model has been tested with two popular hand gesture datasets, namely the Jester dataset and Nvidia dataset. Comparing with other models, our model produced very competitive results. The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.
Proceedings of the SIGCOMM '21 Poster and Demo Sessions
Policy information in computer networking today is hard to manage. This is in sharp contrast to r... more Policy information in computer networking today is hard to manage. This is in sharp contrast to relational data structured in a database that allows easy access. In this demonstration, we ask why cannot (or how can) turn network policies into relational data. Our key observation is that oftentimes a policy does not prescribe a single "definite" network state, but rather is an "incomplete" description of all the legitimate network states. Based on this idea, we adopt conditional tables and the usual SQL interface (a relational structure developed for incomplete database) as a means to represent and query sets of network states in exactly the same way as a single definite network snapshot. More importantly, like relational tables that improve data productivity and innovation, relational policies allow us to extend a rich set of data mediating methods to address the networking problem of coordinating policies in a distributed environment. CCS CONCEPTS • Networks → Programming interfaces; Network manageability; • Computing methodologies → Reasoning about belief and knowledge.
2022 International Conference on Computer Communications and Networks (ICCCN)
5th Asia-Pacific Workshop on Networking (APNet 2021), 2021
Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks, 2021
IEEE/CAA Journal of Automatica Sinica
Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture re... more Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications. This paper presents a novel deep learning network for hand gesture recognition. The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation. To learn short-term features, each video input is segmented into a fixed number of frame groups. A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot. These two entities are fused and fed into a convolutional neural network (ConvNet) for feature extraction. The ConvNets for all groups share parameters. To learn long-term features, outputs from all ConvNets are fed into a long short-term memory (LSTM) network, by which a final classification result is predicted. The new model has been tested with two popular hand gesture datasets, namely the Jester dataset and Nvidia dataset. Comparing with other models, our model produced very competitive results. The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.
Proceedings of the SIGCOMM '21 Poster and Demo Sessions
Policy information in computer networking today is hard to manage. This is in sharp contrast to r... more Policy information in computer networking today is hard to manage. This is in sharp contrast to relational data structured in a database that allows easy access. In this demonstration, we ask why cannot (or how can) turn network policies into relational data. Our key observation is that oftentimes a policy does not prescribe a single "definite" network state, but rather is an "incomplete" description of all the legitimate network states. Based on this idea, we adopt conditional tables and the usual SQL interface (a relational structure developed for incomplete database) as a means to represent and query sets of network states in exactly the same way as a single definite network snapshot. More importantly, like relational tables that improve data productivity and innovation, relational policies allow us to extend a rich set of data mediating methods to address the networking problem of coordinating policies in a distributed environment. CCS CONCEPTS • Networks → Programming interfaces; Network manageability; • Computing methodologies → Reasoning about belief and knowledge.