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A survey on barrier coverage with sensors
Fan WU,Yang GUI,Zhibo WANG,Xiaofeng GAO,Guihai CHEN
Front. Comput. Sci.. 2016, 10 (6): 968-984.
https://doi.org/10.1007/s11704-016-5532-4
For various applications, sensors are deployed to monitor belt regions to guarantee that every movement crossing a barrier of sensors will be detected in real-time with high accuracy and minimize the need for human support. The barrier coverage problem is introduced to model these requirements, and has been examined thoroughly in the past decades. In this survey, we state the problem definitions and systematically consider sensing models, design issues and challenges in barrier coverage problem. We also review representative algorithms in this survey. Furthermore, we provide discussions on some extensions and variants of barrier coverage problems.
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A survey of sketch based modeling systems
Chao DING,Ligang LIU
Front. Comput. Sci.. 2016, 10 (6): 985-999.
https://doi.org/10.1007/s11704-016-5422-9
As 3D technology, including computer graphics, virtual reality and 3D printing, has been rapidly developed in the past years, 3D models are gaining an increasingly huge demand. Traditional 3D modeling platforms such as Maya and ZBrush, utilize “windows, icons, menus, pointers” (WIMP) interface paradigms for fine-grained control to construct detailed models. However, the modeling progress can be tedious and frustrating and thus too hard for a novice user or even a well trained artist. Therefore, a more intuitive interface is needed. Sketch, an intuitive communication and modeling tool for human beings, becomes the first choice of modeling community. So far, various sketch-based modeling systems have been created and studied. In this paper, we attempt to show how these systems work and give a comprehensive survey. We review and categorize the systems in four aspects: the input, the knowledge they use, the modeling approach and the output. We also discuss about inherent challenges and open problems for researchers in the future.
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DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments
Chengliang WANG,Yayun PENG,Debraj DE,Wen-Zhan SONG
Front. Comput. Sci.. 2016, 10 (6): 1000-1011.
https://doi.org/10.1007/s11704-015-4571-6
In this paper, we have proposed and designed DPHK (data prediction based on HMM according to activity pattern knowledge mined from trajectories), a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowledge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM (hidden Markov model) algorithm to predict the sensor node locations people will arrive at according to the received message. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows: (a) the procedure of DPHK is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.
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Constrained query of order-preserving submatrix in gene expression data
Tao JIANG,Zhanhuai LI,Xuequn SHANG,Bolin CHEN,Weibang LI,Zhilei YIN
Front. Comput. Sci.. 2016, 10 (6): 1052-1066.
https://doi.org/10.1007/s11704-016-5487-5
Order-preserving submatrix (OPSM) has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSMdatasets. However, improvingOPSMquery relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst’s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly focus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index (cIndex) and enumerating sequence index (esIndex) based queries have better performance than brute force search.
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Impact of preprocessing on medical data classification
Sarab ALMUHAIDEB,Mohamed El Bachir MENAI
Front. Comput. Sci.. 2016, 10 (6): 1082-1102.
https://doi.org/10.1007/s11704-016-5203-5
The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics ofmedical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations considered include the discretization of numeric attributes, the selection of attribute subset(s), and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.
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FSPM-P: towards a general functional-structural plant model for robust and comprehensive model development
Michael HENKE,Winfried KURTH,Gerhard H. BUCK-SORLIN
Front. Comput. Sci.. 2016, 10 (6): 1103-1117.
https://doi.org/10.1007/s11704-015-4472-8
In the last decade, functional-structural plant modelling (FSPM) has become a more widely accepted paradigm in crop and tree production, as 3D models for the most important crops have been proposed. Given the wider portfolio of available models, it is now appropriate to enter the next level in FSPM development, by introducing more efficient methods for model development. This includes the consideration of model reuse (by modularisation), combination and comparison, and the enhancement of existing models. To facilitate this process, standards for design and communication need to be defined and established. We present a first step towards an efficient and general, i.e., not speciesspecific FSPM, presently restricted to annual or bi-annual plants, but with the potential for extension and further generalization. Model structure is hierarchical and object-oriented, with plant organs being the base-level objects and plant individual and canopy the higher-level objects. Modules for the majority of physiological processes are incorporated, more than in other platforms that have a similar aim (e.g., photosynthesis, organ formation and growth). Simulation runs with several general parameter sets adopted from the literature show that the present prototypewas able to reproduce a plausible output range for different crops (rapeseed, barley, etc.) in terms of both the dynamics and final values (at harvest time) of model state variables such as assimilate production, organ biomass, leaf area and architecture.
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Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern
Qicong WANG,Binbin WANG,Xinjie HAO,Lisheng CHEN,Jingmin CUI,Rongrong JI,Yunqi LEI
Front. Comput. Sci.. 2016, 10 (6): 1118-1129.
https://doi.org/10.1007/s11704-016-5024-6
To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.
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Base communication model of IP covert timing channels
Changda WANG,Yulin YUAN,Lei HUANG
Front. Comput. Sci.. 2016, 10 (6): 1130-1141.
https://doi.org/10.1007/s11704-016-5089-2
IP covert timing channel (IPCTC) is an unconventional communication channel which attaches time information to the packets of an overt channel as messages carriers, e.g., using different inter-packet delays to transmit messages in a packet-switched network. Although the IPCTCs have many different communication methods, based on the concept of time, we categorized the base communication model of the IPCTCs into three types and then utilized the signal processing theory to build their mathematical models. As a result, the basic characteristics of the IPCTCs’ base model were formally derived. Hence, the characteristics of any IPCTC can be derived from the base models that consist of the IPCTC. Furthermore, a set of approaches was devised to implement the base model of the IPCTCs in a TCP/IP network. Experimental results show the correctness of the proposed base model of the IPCTCs in this paper.
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Packet: a privacy-aware access control policy composition method for services composition in cloud environments
Li LIN,Jian HU,Jianbiao ZHANG
Front. Comput. Sci.. 2016, 10 (6): 1142-1157.
https://doi.org/10.1007/s11704-016-5503-9
Combining different independent cloud services must coordinate their access control policies. Otherwise unauthorized access to composite cloud service can occur when there’s a conflict among different cloud service providers’ access control policies, and then it will bring serious data security and privacy issues. In this paper, we propose Packet, a novel access control policy composition method that can detect and resolve policy conflicts in cloud service composition, including those conflicts related to privacyaware purposes and conditions. The Packet method is divided into four steps. First, employing a unified description, heterogeneous policies are transformed into a unified attributebased format. Second, to improve the conflict detection efficiency, policy conflicts on the same resource can be eliminated by adopting cosine similarity-based algorithm. Third, exploiting a hierarchical structure approach, policy conflicts related to different resources or privacy-aware purposes and conditions can be detected. Fourth, different conflict resolution techniques are presented based on the corresponding conflict types. We have successfully implemented the Packet method in Openstack platform. Comprehensive experiments have been conducted, which demonstrate the effectiveness of the proposed method by the comparison with the existing XACML-based system at conflict detection and resolution performance.
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13 articles
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