Non-leaf Nodes Research Articles

Security scheme of XML database service using improved polyphonic splitting

Outsourcing data owner's data to Database Services Provider(DSP) securely provides XML database service for companies and organizations,which is an important data service form in cloud computing.This paper proposed an improved polyphonic splitting scheme for XML database service(IPSS-XML).IPSS-XML overcame the drawback of low verifying efficiency in other existing schemes by adding an Assistant Verifying Data(AVD) to each non-leaf node at low cost.The improvement enhances query executing efficiency without breaking the confidentiality constraints.

A New Optimal Binary Tree SVM Multi-Class Classification Algorithm

A improved binary tree SVM multi-class classification algorithm is proposed. Firstly, constructing the minimum hyper ellipsoid for each class sample in the feather space, and then generating optimal binary tree according to the hyper ellipsoid volume, training sub-classifier for every non-leaf node in the binary tree at the same time. For the sample to be classified, the sub-classifiers are used from the root node until one leaf node, and the corresponding class of the leaf node is the class of the sample. The experiments are done on the Statlog database, and the experimental results show that the algorithm improves classification precision and classification speed, especially in the situation that the number of class are more and their distribution area are equal approximately, the algorithm can greatly improve the classification precision and classification speed.

A New SVM Multi-Class Classification Algorithm Based on Sample Scale and Distribution Area

A new SVM multi-class classification algorithm is proposed. Firstly, the optimal binary tree is constructed by the scale and the distribution area of every class sample, and then the sub-classifiers are trained for every non-leaf node in the binary tree. For the sample to be classified, the classification is done from the root node until someone leaf node, and the corresponding class of the leaf node is the class of the sample. The experimental results show that the algorithm improves the classification precision and classification speed, especially in the situation that the sample scale is less but its distribution area is bigger, the algorithm can improve greatly the classification performance.

The one-against-all partition based binary tree support vector machine algorithms for multi-class classification

The binary tree support vector machine (SVM) algorithm is one of the mainstream algorithms for multi-class classification in the fields of pattern recognition and machine learning. In order to reduce the training and testing time of one-against-all SVM (OAA-SVM) and reduced OAA-SVM (R-OAA-SVM), in this study, two OAA partition based binary tree SVM algorithms are proposed for multi-class classification. One is the single-space-mapped binary tree SVM (SBT-SVM) and the other is the multi-space-mapped binary tree SVM (MBT-SVM). In the proposed two algorithms, the best OAA partition is determined for each non-leaf node and the k-fold cross validation strategy is adopted to obtain the optimal classifiers. A set of experiments is conducted on nine UCI datasets and two face recognition datasets to demonstrate their performances. The results show that in term of testing accuracy, MBT-SVM is comparable with one-against-one SVM (OAO-SVM), R-OAA-SVM and OAA-SVM and superior to SBT-SVM. In term of testing time, MBT-SVM is superior to OAO-SVM, binary tree of SVM (BTS), R-OAA-SVM and OAA-SVM and slightly longer than SBT-SVM. In term of training time, MBT-SVM is superior to BTS, R-OAA-SVM and OAA-SVM and comparable with SBT-SVM. For the datasets with smaller class number and training sample number, the training time of MBT-SVM is comparable with that of OAO-SVM. For the datasets with larger class number or training sample number, in most cases, the training time of MBT-SVM is longer than that of OAO-SVM.

Scenario tree generation approaches using K-means and LP moment matching methods

We consider in this paper the efficient ways to generate multi-stage scenario trees. A general modified K-means clustering method is first presented to generate the scenario tree with a general structure. This method takes the time dependency of the simulated path into account. Based on the traditional and modified K-means analyses, the moment matching of multi-stage scenario trees is described as a linear programming (LP) problem. By simultaneously utilizing simulation, clustering, non-linear time series and moment matching skills, a sequential generation method and another new hybrid approach which can generate the whole multi-stage tree right off are proposed. The advantages of these new methods are: the vector autoregressive and multivariate generalized autoregressive conditional heteroscedasticity (VAR-MGARCH) model is adopted to properly reflect the inter-stage dependency and the time-varying volatilities of the data process, the LP-based moment matching technique ensures that the scenario tree generation problem can be solved more efficiently and the tree scale can be further controlled, and in the meanwhile, the statistical properties of the random data process are maintained properly. What is more important, our new LP methods can guarantee at least two branches are derived from each non-leaf node and thus overcome the drawback in relevant papers. We carry out a series of numerical experiments and apply the scenario tree generation methods to a portfolio management problem, which demonstrate the practicality, efficiency and advantages of our new approaches over other models or methods.

异构无线传感器网络中一种可扩展的代码分发技术

Code dissemination is currently a major research issue in wireless sensor networks (WSNs). Many studies focus on code dissemination in homogeneous WSNs, mainly using a broadcast approach to solve this problem; few studies on code dissemination in heterogeneous WSNs. Furthermore, broadcasting cannot readily be used to solve the heterogenous WSN code dissemination problem directly, which is where we have focused our attention. We transformed this problem into a minimum non-leaf nodes (MNN) Steiner tree problem. We designed a scalable multicast protocol, named Heterogeneous Sensor Networks Scalable Reprogramming Protocol (HSR) to solve the MNN problem. HSR can build different multicast trees according to different nodes or code modules to disseminate different codes to them. HSR is able to approximate the MNN tree problem to a ratio of ln|<italic>R</italic>|(<italic>R</italic> is the set of all destinations) best known lowest bound. Therefore, the communication cost is significantly decreased and the total energy required by WSNs is reduced. We further designed two scalable schemes, special routing log and hops-restricted local broadcast, which compress the multicast tree information and deliver the multicast messages without loss. We also designed a 3-stage pipeline to speed up the transmission of packets, which alleviated interference and hidden terminal issues. We evaluated our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that HSR outperforms previous protocols including the most recent studies on Sprinkler and uCast.

A scalable code dissemination protocol in heterogeneous wireless sensor networks

Code dissemination is currently a major research issue in wireless sensor networks (WSNs). Many studies focus on code dissemination in homogeneous WSNs, mainly using a broadcast approach to solve this problem; few studies on code dissemination in heterogeneous WSNs. Furthermore, broadcasting cannot readily be used to solve the heterogenous WSN code dissemination problem directly, which is where we have focused our attention. We transformed this problem into a minimum non-leaf nodes (MNN) Steiner tree problem. We designed a scalable multicast protocol, named Heterogeneous Sensor Networks Scalable Reprogramming Protocol (HSR) to solve the MNN problem. HSR can build different multicast trees according to different nodes or code modules to disseminate different codes to them. HSR is able to approximate the MNN tree problem to a ratio of ln|R| (R is the set of all destinations) best known lowest bound. Therefore, the communication cost is significantly decreased and the total energy required by WSNs is reduced. We further designed two scalable schemes, special routing log and hops-restricted local broadcast, which compress the multicast tree information and deliver the multicast messages without loss. We also designed a 3-stage pipeline to speed up the transmission of packets, which alleviated interference and hidden terminal issues. We evaluated our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that HSR outperforms previous protocols including the most recent studies on Sprinkler and uCast.

Resilient peer-to-peer streaming of scalable video over hierarchical multicast trees with backup parent pools

This paper introduces a new cross layer tree-based peer-to-peer design using hierarchical cluster layers and a new method for selection of “backup parent pools” for resilient streaming of scalable video to provide highest quality of experience for all peers. Backup parent pools are selected during the process of multicast tree construction based on information provided by the hierarchical clusters. The proposed tree construction method aims to minimize bottlenecks that may be caused by non-leaf nodes with low upload bandwidth. Performance of the proposed system is demonstrated by extensive test results using a wide range of simulation scenarios. Comparison of the results with those of some recent works indicates that the proposed system is clearly superior in several aspects.

On the heterogeneous postal delivery model for multicasting

The heterogeneous postal delivery model assumes that each intermediate node in the multicasting tree incurs a constant switching time for each message that is sent. We have proposed a new model where we assume a more generalized switching time at intermediate nodes. In our model, a child node v of a parent u has a switching delay vector, where the ith element of the vector indicates the switching delay incurred by u for sending the message to v after sending the message to i - 1 other children of u. Given a multicast tree and switching delay vectors at each non-root node in the tree, we provide an O(n5/2) optimal algorithm that will decide the order in which the internal (non-leaf) nodes have to send the multicast message to its children in order to minimize the maximum end-to-end delay due to multicasting. We also show an important lower bound result that optimal multicast switching delay problem is as hard as min-max matching problem on weighted bi- partite graphs and hence O(n5/2) running time is tight.

과거 위치 색인에서 입력/검색 비용 조정을 위한 가변 버퍼 노드 기법 설계

Various applications of LBS (Location Based Services) are being developed to provide the customized service depending on user`s location with progress of wireless communication technology and miniaturization of personalized device. To effectively process an amount of vehicles` location data, LBS requires the techniques such as vehicle observation, data communication, data insertion and search, and user query processing. In this paper, we propose the historical location index, GIP-FB (Group Insertion tree with Flexible Buffer Node) and the flexible buffer node technique to adjust the cost of data insertion and search. the designed GIP+ based index employs the buffer node and the projection storage to cut the cost of insertion and search. Besides, it adjusts the cost of insertion and search by changing the number of line segments of the buffer node with user defined time interval. In the experiment, the buffer node size influences the performance of GIP-FB by changing the number of non-leaf node of the index. the proposed flexible buffer node is used to adjust the performance of the historical location index depending on the applications of LBS.

RSR-tree: A Dynamic Multi-dimensional Index Structure

A new dynamic multi-dimensional index structure called RSR-tree is proposed, in which splitting operations of leaf nodes and non-leaf nodes are performed separatively. RSR-tree retains the sequence ordering of index records in a leaf node of RS-tree and thus reduces the addressing time cost of disk access while reading data files. In addition, it integrates the characteristic of reducing the overlap between brother nodes of R-tree to reduce the query time. The accuracy test and parameter sensitivity test are done on different data sets and the experimental results show that RSR-tree is a dynamic multi-dimensional structure which can efficiently improve query performance and does not bring extra cost of creating index at the same time.

Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a non-functional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20% better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a given conformation. The paper discusses several extensions that may yield further improvements.

A Balanced Multiway Search Tree for Multi-Dimension Searching

A new multi-dimensional index structure called RSR-tree is proposed, which based on RS-tree. In RSR-tree, index records of a leaf node are split to ensure the sequence ordering of index records in a leaf node, which reduces the addressing cost of I/O operations effectively when reading data files. The entries of a non-leaf node are split to decreases the overlap between the brother nodes, which reduces effectively the time of reading data from data files. Experimental results on different data sets show that compared to RS-tree, RSR-tree has better comprehensive performance, in regard to tree building and querying. The querying performance is increased and extra cost is not produced.

Perceptual Guidelines for Creating Rectangular Treemaps

Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hierarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the compared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution of rectangle aspect ratios within a treemap should include non-squares, but should avoid extremes. We then compare treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps and suggest alternate approaches for treemap layout.

A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements

A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distinguish between asymptotic (AS) and osteoarthritis (OA) knee gait patterns and to investigate OA severity using 3-D ground reaction force (GRF) measurements. FDT-SVM incorporates effective techniques for feature selection (FS) and class grouping (CG) at each non-leaf nodes of the tree structure, which reduce the overall complexity of DT building and alleviate the overfitting effect. The embedded FS and CG are based on the notion of fuzzy partition vector (FPV) that comprises the fuzzy membership degrees of every pattern in their target classes, serving as a local evaluation metric with respect to patterns. FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that features are iteratively introduced, providing the maximum additional contribution in regard to the information content given by the previously selected features. A novel Wavelet Packet (WP) decomposition based on the FuzCoC principles is also introduced, to distinguish informative and complementary features from GRF data. The quality of our method is validated in terms of statistical metrics drawn by confusion matrices, such as sensitivity, specificity and total classification accuracy. In addition, we investigate the impact of each GRF component. Finally, comparative results with existing techniques are given, demonstrating the efficacy of the suggested approach.

SOA services selection based on binary-tree-coding genetic algorithm

A binary-tree-coding genetic algorithm (BTGA) was proposed for optimizing services selection during Service-Oriented Architecture (SOA) composition. The services composition plan was transferred into an AOV graph equivalently, and then was transformed into a binary tree further. The binary tree was traversed in postorder, and then encoded. The encoding scheme was based on binary-tree. Since Non-leaf nodes of the tree keep the QoS information of its sub-tree, its calculation load was decreased. It also can represent branch architecture, and overcome the limits of One Dimension coding Genetic Algorithm (ODGA). Simulation results show that BTGA works more efficiently than ODGA.

Novel connected dominating set algorithm based on minimum spanning tree

Through in-depth analysis,an important property of Connected Dominating Set(CDS) in wireless network was obtained,in which the smallest CDS of a simple connected graph is the non-leaf nodes of a spanning tree with most leaf nodes.A new CDS algorithm was designed on the basis of the analysis.Simulation results show that the algorithm possess better performance than others.

R-Tree Index Structure for Multi-Scale Representation of Spatial Data

Aiming at multi-scale representation of spatial data not supported by R-tree, a transmutation index structure of R-tree used for multi-scale representation of spatial data is proposed: (1) Spatial objects may appear at non-leaf nodes; (2) Depth of the tree is made use for reflecting change of spatial resolutions, and resolution dimensions is supported; (3) Structure of the tree's branches supports algorithms of automatic cartography generalization. Query of spatial datas among multiple scales of based on the R-tree's transmutation index structure is analyzed, and constraint conditions, insert algorithms and divide algorithms among creations of the index structure are emphasized. For the same data source, compared with multi-scale index based on quadtree for spatial data, experiments prove that the transmutation structure can search spatial data organized by multi-resolution and memorize generalization results with high efficiency.

Linear algorithm for lexicographic enumeration of CFG parse trees

We study CFG parse tree enumeration in this paper. By dividing the set of all parse trees into infinite hierarchies according to height of parse tree, the hierarchical lexicographic order on the set of parse trees is established. Then grammar-based algorithms for counting and enumerating CFG parse trees in this order are presented. To generate a parse tree of height n, the time complexity is O(n). If τ is a lowest parse tree for its yield, then O(n) =O(∥ gt ∥+ 1), where ∥τ ∥ is the length of the sentence (yield) generated by τ. The sentence can be obtained as a by-product of the parse tree. To compute sentence from its parse tree (needn’t be lowest one), the time complexity is O(node)+O(∥τ ∥ + 1), where node is the number of non-leaf nodes of parse tree τ. To generate both a complete lowest parse tree and its yield at the same time, the time complexity is O(∥τ ∥ + 1).

Min-degree constrained minimum spanning tree problem: New formulation via Miller–Tucker–Zemlin constraints

Given an undirected network with positive edge costs and a positive integer d > 2 , the minimum-degree constrained minimum spanning tree problem is the problem of finding a spanning tree with minimum total cost such that each non-leaf node in the tree has a degree of at least d . This problem is new to the literature while the related problem with upper bound constraints on degrees is well studied. Mixed-integer programs proposed for either type of problem is composed, in general, of a tree-defining part and a degree-enforcing part. In our formulation of the minimum-degree constrained minimum spanning tree problem, the tree-defining part is based on the Miller–Tucker–Zemlin constraints while the only earlier paper available in the literature on this problem uses single and multi-commodity flow-based formulations that are well studied for the case of upper degree constraints. We propose a new set of constraints for the degree-enforcing part that lead to significantly better solution times than earlier approaches when used in conjunction with Miller–Tucker–Zemlin constraints.