Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing

Laboratory of, China

Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing

Laboratory of, China
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Liao X.-W.,Fuzhou University | Liao X.-W.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Chen X.-J.,Fuzhou University | Chen X.-J.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 5 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2017

Mining opinion targets and opinion words is a fundamental task for the Chinese online media to mine opinion and analyze sentiment. The key to enhancing the effectiveness of opinion target and opinion word is to integrate syntactic relations and co-occurrence relations between opinion target and opinion word into the mining model. A novel approach based on a multi-layer relation graph model is proposed to extract opinion targets and opinion words from Chinese social media. First, the word alignment model is employed to extract the candidates of opinion target and opinion word candidates. Second, a multi-layer relation graph is constructed by the syntactic inter-relations between opinion target and opinion word, the co-occurrence intra-relations among opinion targets, and the co-occurrence intra-relations among opinion words. Third, a random-walk algorithm is adopted to calculate the confidence of each opinion target candidate and opinion word candidate. Finally, opinion targets and opinion words are extracted according to their confidence values. Experimental results show that the presented method can not only achieve significant improvement over previous methods, but also have good robustness. Copyright © 2017 Acta Automatica Sinica. All rights reserved.


Liu Z.,Fuzhou University | Liu Z.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Zeng X.,Fuzhou University | Zeng X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 7 more authors.
Proceedings - 15th International Symposium on Parallel and Distributed Computing, ISPDC 2016 | Year: 2017

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a mobile application by executing some parts of the application on a remote server. Recent researches on mobile cloud computing mainly focus on the code partitioning and offloading techniques, assuming that mobile codes are offloaded to a prepared server. However, the context of a mobile device, such as locations and network conditions, changes continuously as it moves throughout the day, and there are multiple options of cloud resources, including remote cloud computing services and nearby cloudlets. In order to offload computation to the cloud resource with powerful processors as well as fast network connection, it needs to dynamically select the appropriate cloud resource and then offload mobile codes to it at runtime, according to the context of the mobile device and possible cloud resources. In this paper, we present a framework for context-aware computation offloading. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically select the cloud resource for computation offloading. Runtime data about computation tasks, contexts of the mobile device and possible cloud resources is collected and modeled at client side, in order to make an optimal offloading decision. A thorough evaluation on two real-world applications is proposed, and the results show that our approach can help reduce execution time by 6%-96% and power consumption by 60%-96% for computation-intensive applications. © 2016 IEEE.


Chen X.,Fuzhou University | Chen X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Zhang W.,Peking University | Huang G.,Peking University | And 6 more authors.
Ruan Jian Xue Bao/Journal of Software | Year: 2014

Wireless sensor network (WSN) plays an important role in the field of IOT (Internet of things), which performs the function of information perception. Thousands of devices as well as sensors are spread in specific areas to collect all kinds of physical information to pass onto the Internet. However, the data gethered from sensors' interfaces is real-time, extremely large and unstructured, hence requiring great effort in mapping to the conceptual application layer. To customize and develop IOT systems more efficiently, this paper proposes an approach based on runtime model to managing wireless sensor networks. First, manageability of wireless sensors is abstracted as runtime models which automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models. Second, a composite model of wireless sensors is constructed through merging their runtime models in order to manage different kinds of devices in a unified way. Third, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and the composite model is ensured through model transformation. Thus, all the management tasks can be carried through executing operating programs on the customized model. The feasibility and efficiency of the approach are validated through a real case study of smart community. © Copyright 2014, Institute of Software, the Chinese Academy of Sciences. All rights reserved.


Chen X.,Fuzhou University | Chen X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Li A.,Fuzhou University | Li A.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 6 more authors.
Frontiers of Computer Science | Year: 2015

The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. However, application development is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires programmers to focus on low-level system issues. The underlying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manageability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization between the customized model and sensor device runtime models is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world application scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application development. © 2015, Higher Education Press and Springer-Verlag Berlin Heidelberg.


Li A.,Fuzhou University | Chen X.,Fuzhou University | Xu Y.,State Grid Corporation of China | Zeng X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 2 more authors.
Proceedings - 2015 12th Web Information System and Application Conference, WISA 2015 | Year: 2015

In recent years, with the rapid development of Internet, those Query-Intensive web information systems can not meet the requirement of high performance, which mainly comes from the following two aspects. First, most database systems that web information systems use store the related data in disks, which causes the slow data access. Second, it is hard to support large numbers of users accessing in parallel due to the limitation of single server's physical resources, which causes the server overload. For solving the problems, this paper proposes a MMDB (Main-Memory Database) cluster based cloud platform for Query-Intensive web information systems. First, we design a MMDB cluster based web information system according to the current technology. Second, we develop a model-based cloud platform, which provides support for the MMDB cluster based web information system at an architecture level. The experiment on a real-world cloud demonstrates the feasibility, effectiveness and benefits of the new approach to improving the performance of those Query-Intensive web information systems. © 2015 IEEE.


Huang Y.,Fuzhou University | Huang Y.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Lan X.,Fuzhou University | Lan X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 2 more authors.
Proceedings - 2015 12th Web Information System and Application Conference, WISA 2015 | Year: 2015

Hadoop is an open source software framework of distributed processing of big data. There are many kinds of services in Hadoop ecosystem, such as HDFS, Map-Reduce, HBase, Hive, Yarn, Flume, Spark, Storm, Zookeeper, and so on, which increase the complexity of deployment and configuration. It takes plenty of time to construct a Hadoop cluster. Although there are some management tools which help administrators deploy and configure Hadoop clusters automatically, they usually provide a fixed solution. So administrators couldn't construct their Hadoop clusters according to different management requirements by the tools. Software architecture acts as a bridge between requirements and implementations. It has been used to reduce the complexity and cost mainly resulted from the difficulties faced by understanding the large-scale and complex software system. This paper proposes a model based approach to Hadoop deployment and configuration which help administrators construct Hadoop clusters in a simple but powerful enough manner. First, we provide the unified models of Hadoop software architecture, according to the domain knowledge of current Hadoop deployment and configuration. Second, we provide a framework with a set of definable rules for domain experts to describe their solutions to deploy and configure Hadoop clusters. Thus, administrators can use various custom solutions to automatically deploy and configure their Hadoop clusters according to different management requirements. In addition, a real-world experiment demonstrates the feasibility, effectiveness and benefits of the new approach to Hadoop deployment and configuration. © 2015 IEEE.


Zeng X.,Fuzhou University | Zeng X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Lan X.,Fuzhou University | Lan X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 3 more authors.
Proceedings - International Computer Software and Applications Conference | Year: 2015

Cloud management becomes increasingly complex and brings high costs, especially with the advent of hybrid cloud. In a hybrid cloud, numerous resources like Virtual Machines (VMs) and Physical Machines in different clouds have to be managed together to make the whole hybrid cloud work cost-effectively. For controlling the management cost, in particular the manual management cost, many programs have been developed to take over manual management tasks or reduce their complexity and difficulty. These programs are usually hard-coded by languages like Java and C++, which bring enough capability and flexibility but also cause high programming effort and cost. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing a hybrid cloud management system based on the architecture-based runtime models of clouds can benefit from the model-specific natures, and thus reduce the development workload. This paper proposes a runtime architecture based approach to developing the management programs in a simple but powerful enough manner. First of all, the manageability (such as APIs, configuration files and scripts) of different kinds of clouds, is abstracted as a runtime architecture based model of cloud software architecture, which can automatically and immediately propagate any observable runtime changes of the target platforms to the corresponding architecture models, and vice versa. Second, we provide a unified model of cloud software architecture, according to the domain knowledge of current cloud platforms, such as Cloud Stack, Open Stack and Eucalyptus. Third, the synchronization between the unified model and cloud runtime models is ensured through model transformation, thus, all the management tasks of the hybrid cloud, could be carried out through executing programs on the unified model, which decreases the complexity of use and management. The experiment on a real-world hybrid cloud demonstrates the feasibility, effectiveness and benefits of the new approach to managing hybrid clouds. © 2015 IEEE.


Lan X.,Fuzhou University | Lan X.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Liu Y.,Fuzhou University | Liu Y.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | And 8 more authors.
Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom | Year: 2015

Cloud Computing is evolving as a key computing paradigm for sharing resources. One type of Cloud, which provides platform resources including all the elements of application runtime environment, is regarded as PaaS Cloud. The management of PaaS Cloud is a complex task, up to the point, where manual operation is hard to be cost effective. As the application runtime environment is supported by a set of dynamically composed, distributed elements. What is more, in order to achieve a management target, multiple operations have to be applied over the distributed and heterogeneous elements of PaaS Cloud. To improve the management of PaaS Cloud, this paper proposes to support the configuration and deployment of application runtime environment in PaaS Cloud with an autonomous engine. The automation is enabled by the definition of a domain-specific information model, which captures all the related information with the same abstractions, describing the application runtime environment, PaaS Cloud infrastructure and management targets. On top of that, a technique based on Satisfiability is described, which automatically analyses the state of the managed objects and plans required operations for maintaining it. The result from a case study is provided to validate the feasibility of this approach. © 2014 IEEE.


Chen K.,Fuzhou University | Chen K.,Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing | Lin C.,Fuzhou University | Zhong S.,Fuzhou University | And 2 more authors.
Computer Science and Information Systems | Year: 2015

The Spatial Rich Model (SRM) generates powerful steganalysis features, but has high computational complexity since it requires calculating tens of thousands of convolutions with image noise residuals. Practical applications dealing with a massive amount of image transferred through the Internet would suffer a long computing time if using CPU. To accelerate the steganalysis, we present a parallel SRM feature extraction algorithm based on GPU architecture. We exploit parallelism of the algorithm, modify the original SRM extraction algorithm and employ some strategies to avoid the disadvantage of its sequentiality. Some OpenCL optimization technologies are also used to accelerate the extraction process, such as convolution unrolling, combined memory access, split-merge strategy for co-occurrence matrix calculation. The experimental results show that the speed of the proposed parallel extraction algorithm for different size images is 25~55 times faster than the original single thread algorithm. In addition, when using AMD GPU HD 6850, our algorithm runs 2~4.2 times faster than using a Intel Quad-core CPU. This indicates our algorithm makes good use of the GPU cores. © 2015 ComSIS Consortium. All rights reserved.

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