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Bike sharing systems (BSSs) are widely adopted in major cities of the world due to traffic congestion and carbon emissions. Although there have been approaches to exploit either bike trailers via crowdsourcing or carrier vehicles to reposition bikes in the “right” stations in the “right” time, they did not jointly consider the usage of both bike trailers and carrier vehicles. In this paper, we aim to take advantage of both bike trailers and carrier vehicles to reduce the loss of demand by determining whether bike trailers or carrier vehicles (or both) should be used. In addition, we also would like to maximize the overall profit with regard to the crowdsourcing of bike trailers and the fuel cost of carrier vehicles. In the experiment, we exhibit that our approach outperforms baselines in multiple data sets from bike sharing companies.
Xinghua Zheng; Ming Tang; Yuechang Liu; ZhengZheng Xian; Hankz Zhuo. Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems. Applied Sciences 2021, 11, 7227 .
AMA StyleXinghua Zheng, Ming Tang, Yuechang Liu, ZhengZheng Xian, Hankz Zhuo. Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems. Applied Sciences. 2021; 11 (16):7227.
Chicago/Turabian StyleXinghua Zheng; Ming Tang; Yuechang Liu; ZhengZheng Xian; Hankz Zhuo. 2021. "Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems." Applied Sciences 11, no. 16: 7227.
Synthesizing plans for a deformable object to transit from initial observations to goal observations, both of which are represented by high-dimensional data (namely “raw” data), is challenging due to the difficulty of learning abstract state representations of raw data and transition models of continuous states and continuous actions. Even though there have been some approaches making remarkable progress regarding the planning problem, they often neglect actions between observations and are unable to generate action sequences from initial observations to goal observations. In this paper, we propose a novel algorithm framework, namely AGN. We first learn a state-abstractor model to abstract states from raw observations, a state-generator model to generate raw observations from states, a heuristic model to predict actions to be executed in current states, and a transition model to transform current states to next states after executing specific actions. After that, we directly generate plans for a deformable object by performing the four models. We evaluate our approach in continuous domains and show that our approach is effective with comparison to state-of-the-art algorithms.
Ziqi Sheng; Kebing Jin; Zhihao Ma; Hankz-Hankui Zhuo. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors 2021, 21, 4552 .
AMA StyleZiqi Sheng, Kebing Jin, Zhihao Ma, Hankz-Hankui Zhuo. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors. 2021; 21 (13):4552.
Chicago/Turabian StyleZiqi Sheng; Kebing Jin; Zhihao Ma; Hankz-Hankui Zhuo. 2021. "Action Generative Networks Planning for Deformable Object with Raw Observations." Sensors 21, no. 13: 4552.
The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models.
Liming Gao; Huiling Zhu; Hankz Zhuo; Jin Xu. Dual Quaternion Embeddings for Link Prediction. Applied Sciences 2021, 11, 5572 .
AMA StyleLiming Gao, Huiling Zhu, Hankz Zhuo, Jin Xu. Dual Quaternion Embeddings for Link Prediction. Applied Sciences. 2021; 11 (12):5572.
Chicago/Turabian StyleLiming Gao; Huiling Zhu; Hankz Zhuo; Jin Xu. 2021. "Dual Quaternion Embeddings for Link Prediction." Applied Sciences 11, no. 12: 5572.
Topic models have been widely used in discovering latent topics which are shared across documents in text mining. Vector representations, word embeddings and topic embeddings, map words and topics into a low-dimensional and dense real-value vector space, which have obtained high performance in NLP tasks. However, most of the existing models assume the results trained by one of them are perfect correct and used as prior knowledge for improving the other model. Some other models use the information trained from external large corpus to help improving smaller corpus. In this paper, we aim to build such an algorithm framework that makes topic models and vector representations mutually improve each other within the same corpus. An EM-style algorithm framework is employed to iteratively optimize both topic model and vector representations. Experimental results show that our model outperforms state-of-the-art methods on various NLP tasks.
Jarvan Law; Hankz Hankui Zhuo; Junhua He; Erhu Rong. LTSG: Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 375 -387.
AMA StyleJarvan Law, Hankz Hankui Zhuo, Junhua He, Erhu Rong. LTSG: Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():375-387.
Chicago/Turabian StyleJarvan Law; Hankz Hankui Zhuo; Junhua He; Erhu Rong. 2018. "LTSG: Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 375-387.
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.
Xiaomu Luo; Huoyuan Tan; Qiuju Guan; Tong Liu; Hankz Hankui Zhuo; Baihua Shen. Abnormal Activity Detection Using Pyroelectric Infrared Sensors. Sensors 2016, 16, 822 .
AMA StyleXiaomu Luo, Huoyuan Tan, Qiuju Guan, Tong Liu, Hankz Hankui Zhuo, Baihua Shen. Abnormal Activity Detection Using Pyroelectric Infrared Sensors. Sensors. 2016; 16 (6):822.
Chicago/Turabian StyleXiaomu Luo; Huoyuan Tan; Qiuju Guan; Tong Liu; Hankz Hankui Zhuo; Baihua Shen. 2016. "Abnormal Activity Detection Using Pyroelectric Infrared Sensors." Sensors 16, no. 6: 822.