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Zhiping Cai
College of Computer, National University of Defense Technology, Changsha 417003, China

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Journal article
Published: 25 August 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods, such as label-feature concatenation, to perform coarse-grained semantic fusion among cross-modal instances but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this article, we propose a dynamic cross-modal attention network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to dynamic model dense interactions over token-label spaces, in which two basic attention units and a novel two-phase prediction are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and label-to-token). Experiment results on the CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 91.72% F1 on entity recognition and 73.46% F1 on relation classification. In the ADE and DREC datasets, our model surpasses existing approaches by more than 2.1% and 2.54% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

ACS Style

Shan Zhao; Minghao Hu; Zhiping Cai; Fang Liu. Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation Extraction. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -10.

AMA Style

Shan Zhao, Minghao Hu, Zhiping Cai, Fang Liu. Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation Extraction. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-10.

Chicago/Turabian Style

Shan Zhao; Minghao Hu; Zhiping Cai; Fang Liu. 2021. "Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation Extraction." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-10.

Journal article
Published: 14 August 2021 in Computer Networks
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The graph stream has recently arisen in many interactive scenarios. Characterizing with large volume and high dynamic, graph streams are known to be difficult for high-speed summary and analysis, especially provided with limited resource availability. Existing solutions mainly use sketch-based methods to estimate the weight of items (e.g., Count-Min Sketch) and preserve the underlying graph structure information (e.g., TCM). Unfortunately, these solutions neither support complex graph-based queries nor achieve efficient real-time queries. In view of these limitations, we design DMatrix, a novel 3-dimensional graph sketch to facilitate fast and accurate queries in graph stream. Both structural query and weight-based estimation are supported with DMatrix. Through the integration of representative key reservation and majority voting, DMatrix can effectively narrow the error bounds of queries with real-time response efficiency. Both theoretical analysis and experimental results confirm that our solution is superior in accuracy and efficiency comparing with the state-of-the-art.

ACS Style

Changsheng Hou; Bingnan Hou; Tongqing Zhou; Zhiping Cai. DMatrix: Toward fast and accurate queries in graph stream. Computer Networks 2021, 198, 108403 .

AMA Style

Changsheng Hou, Bingnan Hou, Tongqing Zhou, Zhiping Cai. DMatrix: Toward fast and accurate queries in graph stream. Computer Networks. 2021; 198 ():108403.

Chicago/Turabian Style

Changsheng Hou; Bingnan Hou; Tongqing Zhou; Zhiping Cai. 2021. "DMatrix: Toward fast and accurate queries in graph stream." Computer Networks 198, no. : 108403.

Journal article
Published: 05 July 2021 in International Journal of Intelligent Systems
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ACS Style

Haiwen Chen; Huan Zhou; Jiaping Yu; Kui Wu; Fang Liu; Tongqing Zhou; Zhiping Cai. Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Haiwen Chen, Huan Zhou, Jiaping Yu, Kui Wu, Fang Liu, Tongqing Zhou, Zhiping Cai. Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Haiwen Chen; Huan Zhou; Jiaping Yu; Kui Wu; Fang Liu; Tongqing Zhou; Zhiping Cai. 2021. "Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain." International Journal of Intelligent Systems , no. : 1.

Research article
Published: 03 June 2021 in International Journal of Intelligent Systems
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With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two-stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster-based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

ACS Style

Haiwen Chen; Jiaping Yu; Huan Zhou; Tongqing Zhou; Fang Liu; Zhiping Cai. SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Haiwen Chen, Jiaping Yu, Huan Zhou, Tongqing Zhou, Fang Liu, Zhiping Cai. SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Haiwen Chen; Jiaping Yu; Huan Zhou; Tongqing Zhou; Fang Liu; Zhiping Cai. 2021. "SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience." International Journal of Intelligent Systems , no. : 1.

Journal article
Published: 26 April 2021 in IEEE Internet of Things Journal
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With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing computational burden when the edge is overloaded. In this paper, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Specially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multi-objective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios respectively to reveal the advantages of the proposed framework.

ACS Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Qiang Ni; Tongqing Zhou; Jiaping Yu; Haiwen Chen; Fang Liu. Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game. IEEE Internet of Things Journal 2021, PP, 1 -1.

AMA Style

Hongjia Wu, Jiao Zhang, Zhiping Cai, Qiang Ni, Tongqing Zhou, Jiaping Yu, Haiwen Chen, Fang Liu. Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game. IEEE Internet of Things Journal. 2021; PP (99):1-1.

Chicago/Turabian Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Qiang Ni; Tongqing Zhou; Jiaping Yu; Haiwen Chen; Fang Liu. 2021. "Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game." IEEE Internet of Things Journal PP, no. 99: 1-1.

Journal article
Published: 11 January 2021 in IEEE Transactions on Network and Service Management
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Network anomalies, such as wide-area congestion and packet loss, can seriously degrade network performance. To this end, it is critical to accurately identify network anomalies on end-to-end paths for high quality network services in practice. In this work, we propose an unsupervised two-step method for the detection and characterization of general network anomalies. It first finds the change-points in large-scale RTT time series by formalizing an optimization problem in terms of data series segmentation. Then we mark the segments as normal or abnormal on different sides of a change-point through exploitation of their distribution statistics. After detecting an anomaly, a further step is introduced to analyze the relations between links with state changes and localize the entities (nodes or links) that most likely cause the corresponding event. We believe such unsupervised and light-weighed method can provide valuable insights on anomaly mining in large-scale time series data. Extensive experiments on both simulated (artificial time series with ground truth) and real-network (RIPE Atlas traceroute measurements) datasets are performed. The results demonstrate that the proposed method can achieve better performance, w.r.t. accuracy and efficiency, than existing solutions.

ACS Style

Bingnan Hou; Changsheng Hou; Tongqing Zhou; Zhiping Cai; Fang Liu. Detection and Characterization of Network Anomalies in Large-Scale RTT Time Series. IEEE Transactions on Network and Service Management 2021, 18, 793 -806.

AMA Style

Bingnan Hou, Changsheng Hou, Tongqing Zhou, Zhiping Cai, Fang Liu. Detection and Characterization of Network Anomalies in Large-Scale RTT Time Series. IEEE Transactions on Network and Service Management. 2021; 18 (1):793-806.

Chicago/Turabian Style

Bingnan Hou; Changsheng Hou; Tongqing Zhou; Zhiping Cai; Fang Liu. 2021. "Detection and Characterization of Network Anomalies in Large-Scale RTT Time Series." IEEE Transactions on Network and Service Management 18, no. 1: 793-806.

Journal article
Published: 18 December 2020 in IEEE Transactions on Knowledge and Data Engineering
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Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices; and isolate the processes of affinity learning, multi-view information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful to eliminate noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels matrix are learned simultaneously in a unified framework. By doing so, the three steps can negotiate with each other to best serve the clustering task, leading to improved performance. Accordingly, we propose an iterative algorithm to solve the resulting optimization problem. Extensive experiment results on benchmark datasets demonstrate the superiority of our method against other state-of-the-art approaches.

ACS Style

Pei Zhang; Xinwang Liu; Jian Xiong; Sihang Zhou; Wentao Zhao; En Zhu; Zhiping Cai. Consensus One-step Multi-view Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering 2020, PP, 1 -1.

AMA Style

Pei Zhang, Xinwang Liu, Jian Xiong, Sihang Zhou, Wentao Zhao, En Zhu, Zhiping Cai. Consensus One-step Multi-view Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering. 2020; PP (99):1-1.

Chicago/Turabian Style

Pei Zhang; Xinwang Liu; Jian Xiong; Sihang Zhou; Wentao Zhao; En Zhu; Zhiping Cai. 2020. "Consensus One-step Multi-view Subspace Clustering." IEEE Transactions on Knowledge and Data Engineering PP, no. 99: 1-1.

Journal article
Published: 10 October 2020 in Sensors
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With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.

ACS Style

Pei Zhang; Siwei Wang; Jingtao Hu; Zhen Cheng; Xifeng Guo; En Zhu; Zhiping Cai. Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering. Sensors 2020, 20, 5755 .

AMA Style

Pei Zhang, Siwei Wang, Jingtao Hu, Zhen Cheng, Xifeng Guo, En Zhu, Zhiping Cai. Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering. Sensors. 2020; 20 (20):5755.

Chicago/Turabian Style

Pei Zhang; Siwei Wang; Jingtao Hu; Zhen Cheng; Xifeng Guo; En Zhu; Zhiping Cai. 2020. "Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering." Sensors 20, no. 20: 5755.

Journal article
Published: 18 September 2020 in IEEE Transactions on Knowledge and Data Engineering
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Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views. Though demonstrating promising performance in various applications, most of existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct the optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. Also, storing and implementing complex operations on the {$n\times n}$ Laplacian matrices incurs intensive storage and computation complexity. To address these issues, this paper first proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix, and then extends it to the late fusion version for accurate and efficient multi-view clustering. Specifically, our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base Laplacian matrices simultaneously. By this way, the representative capacity of the learned optimal Laplacian matrix is enhanced, which is helpful to better utilize the hidden high-order connection information among data, leading to improved clustering performance. We design an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experimental results on nine datasets demonstrate the superiority of the proposed algorithm

ACS Style

Weixuan Liang; Sihang Zhou; Jian Xiong; Xinwang Liu; Siwei Wang; En Zhu; Zhiping Cai; Xin Xu. Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix. IEEE Transactions on Knowledge and Data Engineering 2020, PP, 1 -1.

AMA Style

Weixuan Liang, Sihang Zhou, Jian Xiong, Xinwang Liu, Siwei Wang, En Zhu, Zhiping Cai, Xin Xu. Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix. IEEE Transactions on Knowledge and Data Engineering. 2020; PP (99):1-1.

Chicago/Turabian Style

Weixuan Liang; Sihang Zhou; Jian Xiong; Xinwang Liu; Siwei Wang; En Zhu; Zhiping Cai; Xin Xu. 2020. "Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix." IEEE Transactions on Knowledge and Data Engineering PP, no. 99: 1-1.

Conference paper
Published: 01 July 2020 in Proceedings of the 2nd International Conference on Data Engineering and Communication Technology
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For Internet-based services quality, it is very necessary for Internet companies to monitor a large number of key performance indicators (KPIs) and accurately detect anomalies. With the increasingly complex structure of the system, the changing characteristics of the performance monitoring data have gradually become a challenge for anomaly detection. Recently, in the performance management sector, there has been renewed interest in research on anomaly detection of KPI streams. There has been a lot of work in the area of clustering-based unsupervised anomaly detection. This paper presents a survey of various clustering-based anomaly detection techniques and discusses the advantages, limitations, and practical significance of different algorithms. Some practical application-related kinds of literature are summarized. At the end of the paper, we put forward some new research trends and opinions and suggestions for the research direction.

ACS Style

Ji Qian; Guangfu Zeng; Zhiping Cai; Shuhui Chen; Ningzheng Luo; Haibing Liu. A Survey on Anomaly Detection Techniques in Large-Scale KPI Data. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2020, 767 -776.

AMA Style

Ji Qian, Guangfu Zeng, Zhiping Cai, Shuhui Chen, Ningzheng Luo, Haibing Liu. A Survey on Anomaly Detection Techniques in Large-Scale KPI Data. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2020; ():767-776.

Chicago/Turabian Style

Ji Qian; Guangfu Zeng; Zhiping Cai; Shuhui Chen; Ningzheng Luo; Haibing Liu. 2020. "A Survey on Anomaly Detection Techniques in Large-Scale KPI Data." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 767-776.

Journal article
Published: 22 June 2020 in Computer Networks
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Knowing the paths between autonomous systems (ASes) is essential to understanding the behavior of the Internet routing system and contributes to many Internet services. Despite Traceroute being a useful tool that can conveniently reveal AS paths, the accuracy is affected by intermediate techniques, and it is impossible to implement Traceroute between arbitrary hosts. There are also methods that infer the AS path using BGP data, but the accuracy cannot always meet our expectations. The most difficult part of AS path inference is how to find the best path among the candidate paths. In this paper, we proposed ProbInfer: a probability-based AS path inference method that from the perspective of multigraph. The probabilistic method can efficiently solve the problem of finding the best path by scoring each candidate path. The multigraph can conveniently stitch AS paths and provide characters used to improve the accuracy of the method. First, ProbInfer separates the candidate paths into two types: SingleShortest (easy to infer) and MultiShortest (hard to infer and the majority). Second, we design the features used to make decisions from three aspects; especially, the features that come from stitching points and the multigraph show great value. Last, we scored the two types of candidate paths using a machine learning algorithm that trains different models for each situation. The results show that compared with state-of-the-art methods, ProbInfer performs much better.

ACS Style

Xionglve Li; Zhiping Cai; Bingnan Hou; Ning Liu; Fang Liu; Jieren Cheng. ProbInfer: Probability-based AS path inference from multigraph perspective. Computer Networks 2020, 180, 107377 .

AMA Style

Xionglve Li, Zhiping Cai, Bingnan Hou, Ning Liu, Fang Liu, Jieren Cheng. ProbInfer: Probability-based AS path inference from multigraph perspective. Computer Networks. 2020; 180 ():107377.

Chicago/Turabian Style

Xionglve Li; Zhiping Cai; Bingnan Hou; Ning Liu; Fang Liu; Jieren Cheng. 2020. "ProbInfer: Probability-based AS path inference from multigraph perspective." Computer Networks 180, no. : 107377.

Journal article
Published: 16 March 2020 in IEEE Internet of Things Journal
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With the widespread application of infotainment services in intelligent connected vehicles (ICV), network traffic has grown exponentially, bringing huge burden and energy consumption to ICV network. Edge caching, which enables edges (e.g., vehicles or roadside units) with cache storages, is a promising technology to alleviate this problem. In this paper, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to roadside unit (V2R), an energy aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and roadside units as well as the size of transmission content, the energy consumption model in ICV network is formulated to implement the optimal selection of cache nodes. Then the selection of cache node in ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy efficiency cache node selection (OEECS). Simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

ACS Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Fang Liu; Yangyang Li; Anfeng Liu. Toward Energy-Aware Caching for Intelligent Connected Vehicles. IEEE Internet of Things Journal 2020, 7, 8157 -8166.

AMA Style

Hongjia Wu, Jiao Zhang, Zhiping Cai, Fang Liu, Yangyang Li, Anfeng Liu. Toward Energy-Aware Caching for Intelligent Connected Vehicles. IEEE Internet of Things Journal. 2020; 7 (9):8157-8166.

Chicago/Turabian Style

Hongjia Wu; Jiao Zhang; Zhiping Cai; Fang Liu; Yangyang Li; Anfeng Liu. 2020. "Toward Energy-Aware Caching for Intelligent Connected Vehicles." IEEE Internet of Things Journal 7, no. 9: 8157-8166.

Journal article
Published: 01 February 2020 in IEEE Transactions on Vehicular Technology
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ACS Style

Jiao Zhang; Li Zhou; Fuhui Zhou; Boon-Chong Seet; Haijun Zhang; Zhiping Cai; Jibo Wei. Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing. IEEE Transactions on Vehicular Technology 2020, 69, 2114 -2125.

AMA Style

Jiao Zhang, Li Zhou, Fuhui Zhou, Boon-Chong Seet, Haijun Zhang, Zhiping Cai, Jibo Wei. Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing. IEEE Transactions on Vehicular Technology. 2020; 69 (2):2114-2125.

Chicago/Turabian Style

Jiao Zhang; Li Zhou; Fuhui Zhou; Boon-Chong Seet; Haijun Zhang; Zhiping Cai; Jibo Wei. 2020. "Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing." IEEE Transactions on Vehicular Technology 69, no. 2: 2114-2125.

Research article
Published: 30 September 2019 in Security and Communication Networks
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Phone number is a unique identity code of a mobile subscriber, which plays a more important role in the mobile social network life than another identification number IMSI. Unlike the IMSI, a mobile device never transmits its own phone number to the network side in the radio. However, the mobile network may send a user’s phone number to another mobile terminal when this user initiating a call or SMS service. Based on the above facts, with the help of an IMSI catcher and 2G man-in-the-middle attack, this paper implemented a practicable and effective phone number catcher prototype targeting at LTE mobile phones. We caught the LTE user’s phone number within a few seconds after the device camped on our rogue station. This paper intends to verify that mobile privacy is also quite vulnerable even in LTE networks as long as the legacy GSM still exists. Moreover, we demonstrated that anyone with basic programming skills and the knowledge of GSM/LTE specifications can easily build a phone number catcher using SDR tools and commercial off-the-shelf devices. Hence, we hope the operators worldwide can completely disable the GSM mobile networks in the areas covered by 3G and 4G networks as soon as possible to reduce the possibility of attacks on higher-generation cellular networks. Several potential countermeasures are also discussed to temporarily or permanently defend the attack.

ACS Style

Chuan Yu; Shuhui Chen; Zhiping Cai. LTE Phone Number Catcher: A Practical Attack against Mobile Privacy. Security and Communication Networks 2019, 2019, 1 -10.

AMA Style

Chuan Yu, Shuhui Chen, Zhiping Cai. LTE Phone Number Catcher: A Practical Attack against Mobile Privacy. Security and Communication Networks. 2019; 2019 ():1-10.

Chicago/Turabian Style

Chuan Yu; Shuhui Chen; Zhiping Cai. 2019. "LTE Phone Number Catcher: A Practical Attack against Mobile Privacy." Security and Communication Networks 2019, no. : 1-10.

Journal article
Published: 23 September 2019 in Journal of Biomedical Informatics
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Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chinese CNER. However, these models do not make full use of the information in EHR, for these models are either word-based or character-based. In addition, neural models tend to be locally unstable and even tiny perturbation may mislead them. In this paper, we firstly propose a novel adversarial training based lattice LSTM with a conditional random field layer (AT-lattice LSTM-CRF) for Chinese CNER. Lattice LSTM is used to capture richer information in EHR. As a powerful regularization method , AT can be used to improve the robustness of neural models by adding perturbations to the training data. Then, we conduct experiments on the proposed neural model with dataset of CCKS-2017 Task 2. The results show that the proposed model achieves a highly competitive performance (with an F1 score of 89.64%) compared to other prevalent neural models, which can be a reinforced baseline for further research in this field

ACS Style

Shan Zhao; Zhiping Cai; Haiwen Chen; Ye Wang; Fang Liu; Anfeng Liu. Adversarial training based lattice LSTM for Chinese clinical named entity recognition. Journal of Biomedical Informatics 2019, 99, 103290 .

AMA Style

Shan Zhao, Zhiping Cai, Haiwen Chen, Ye Wang, Fang Liu, Anfeng Liu. Adversarial training based lattice LSTM for Chinese clinical named entity recognition. Journal of Biomedical Informatics. 2019; 99 ():103290.

Chicago/Turabian Style

Shan Zhao; Zhiping Cai; Haiwen Chen; Ye Wang; Fang Liu; Anfeng Liu. 2019. "Adversarial training based lattice LSTM for Chinese clinical named entity recognition." Journal of Biomedical Informatics 99, no. : 103290.

Journal article
Published: 17 September 2019 in IEEE Transactions on Mobile Computing
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Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos' spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of 33% and 18.7% on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8% improvement on the photo coverage performance.

ACS Style

Tongqing Zhou; Bin Xiao; Zhiping Cai; Ming Xu. A Utility Model for Photo Selection in Mobile Crowdsensing. IEEE Transactions on Mobile Computing 2019, 20, 48 -62.

AMA Style

Tongqing Zhou, Bin Xiao, Zhiping Cai, Ming Xu. A Utility Model for Photo Selection in Mobile Crowdsensing. IEEE Transactions on Mobile Computing. 2019; 20 (1):48-62.

Chicago/Turabian Style

Tongqing Zhou; Bin Xiao; Zhiping Cai; Ming Xu. 2019. "A Utility Model for Photo Selection in Mobile Crowdsensing." IEEE Transactions on Mobile Computing 20, no. 1: 48-62.

Conference paper
Published: 11 July 2019 in Algorithms and Data Structures
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Clustering of heterogeneous medical records plays an extremely important role in understanding pathology, identifying correlations between medical records, and adjuvant treatment of medical records. In view of the instability of the existing medical record clustering algorithm in the processing of heterogeneous medical record data, this paper proposes a medical record clustering algorithm based on fuzzy matrix for integrated structure and unstructured data. Firstly, the algorithm de-correlates the initial data based on the Spearman correlation coefficient to avoid the data correlation error of subsequent analysis. Second, this paper introduces the posterior probability theory for stability weighting, comprehensive structure and unstructured data. Finally, according to fuzzy transitive closure principle, the medical records are clustered from the perspective of relationship transformation. Compared with the existing partial clustering algorithm, the algorithm proposed in this paper improves the clustering accuracy. In addition, it also solves the dynamic and hierarchical problems of medical record clustering to some extent.

ACS Style

Zhenyu Zhang; Wencheng Sun; Zhiping Cai; Ningzheng Luo; Ming Wang. Fuzzy Clustering: A New Clustering Method in Heterogeneous Medical Records Searching. Algorithms and Data Structures 2019, 3 -15.

AMA Style

Zhenyu Zhang, Wencheng Sun, Zhiping Cai, Ningzheng Luo, Ming Wang. Fuzzy Clustering: A New Clustering Method in Heterogeneous Medical Records Searching. Algorithms and Data Structures. 2019; ():3-15.

Chicago/Turabian Style

Zhenyu Zhang; Wencheng Sun; Zhiping Cai; Ningzheng Luo; Ming Wang. 2019. "Fuzzy Clustering: A New Clustering Method in Heterogeneous Medical Records Searching." Algorithms and Data Structures , no. : 3-15.

Conference paper
Published: 26 June 2019 in Proceedings of the IEEE
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Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper.

ACS Style

Fang Liu; Guoming Tang; Youhuizi Li; Zhiping Cai; Xingzhou Zhang; Tongqing Zhou. A Survey on Edge Computing Systems and Tools. Proceedings of the IEEE 2019, 107, 1537 -1562.

AMA Style

Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, Tongqing Zhou. A Survey on Edge Computing Systems and Tools. Proceedings of the IEEE. 2019; 107 (8):1537-1562.

Chicago/Turabian Style

Fang Liu; Guoming Tang; Youhuizi Li; Zhiping Cai; Xingzhou Zhang; Tongqing Zhou. 2019. "A Survey on Edge Computing Systems and Tools." Proceedings of the IEEE 107, no. 8: 1537-1562.

Journal article
Published: 01 January 2019 in Computers, Materials & Continua
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ACS Style

Menghua Luo; Ke Wang; Zhiping Cai; Anfeng Liu; Yangyang Li; Chak Fong Cheang. Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection. Computers, Materials & Continua 2019, 58, 15 -26.

AMA Style

Menghua Luo, Ke Wang, Zhiping Cai, Anfeng Liu, Yangyang Li, Chak Fong Cheang. Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection. Computers, Materials & Continua. 2019; 58 (1):15-26.

Chicago/Turabian Style

Menghua Luo; Ke Wang; Zhiping Cai; Anfeng Liu; Yangyang Li; Chak Fong Cheang. 2019. "Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection." Computers, Materials & Continua 58, no. 1: 15-26.

Research article
Published: 01 January 2019 in Security and Communication Networks
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Due to the proliferation of mobile applications, mobile traffic identification plays a crucial role in understanding the network traffic. However, the pervasive unconcerned apps and the emerging apps pose great challenges to the mobile traffic identification method based on supervised machine learning, since such method merely identifies and discriminates several apps of interest. In this paper we propose a three-layer classifier using machine learning to identify mobile traffic in open-world settings. The proposed method has the capability of identifying traffic generated by unconcerned apps and zero-day apps; thus it can be applied in the real world. A self-collected dataset that contains 160 apps is used to validate the proposed method. The experimental results show that our classifier achieves over 98% precision and produces a much smaller number of false positives than that of the state of the art.

ACS Style

Shuang Zhao; Shuhui Chen; Yipin Sun; Zhiping Cai; Jinshu Su. Identifying Known and Unknown Mobile Application Traffic Using a Multilevel Classifier. Security and Communication Networks 2019, 2019, 1 -11.

AMA Style

Shuang Zhao, Shuhui Chen, Yipin Sun, Zhiping Cai, Jinshu Su. Identifying Known and Unknown Mobile Application Traffic Using a Multilevel Classifier. Security and Communication Networks. 2019; 2019 ():1-11.

Chicago/Turabian Style

Shuang Zhao; Shuhui Chen; Yipin Sun; Zhiping Cai; Jinshu Su. 2019. "Identifying Known and Unknown Mobile Application Traffic Using a Multilevel Classifier." Security and Communication Networks 2019, no. : 1-11.