Non-leaf Nodes Research Articles

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

The tree-child network inference problem for line trees and the shortest common supersequence problem for permutation strings

One strategy for inference of phylogenetic networks is to solve the phylogenetic network problem, which involves inferring phylogenetic trees first and subsequently computing the smallest phylogenetic network that displays all the trees. This approach capitalizes on exceptional tools available for inferring phylogenetic trees from biomolecular sequences. Since the vast space of phylogenetic networks poses difficulties in obtaining comprehensive sampling, the researchers switch their attention to inferring tree-child networks from multiple phylogenetic trees, where in a tree-child network each non-leaf node must have at least one child that is an indegree-one node. Three results are obtained in this work: (1) The shortest common supersequence problem remains NP-hard even for permutation strings. (2) Derived from the first result, the tree-child network inference problem is also established as NP-hard even for line trees (also known as caterpillar trees). (3) The parsimonious tree-child networks that display all the line trees are the same as those displaying all the binary trees and their hybridization number is Θ(n3) for n taxa.

Algorithms for the Reconstruction of Genomic Structures with Proofs of Their Low Polynomial Complexity and High Exactness

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called “structures”, meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity.

Manual-Based Automated Maneuvering Decisions for Air-to-Air Combat

A novel new air combat algorithm is proposed, which is based on the knowledge extracted from the experience of human pilots. First, to implement a fighter that maneuvers based on manual control, the maneuver form of the fighter is analyzed and represented as a block. Second, the blocks for each function are connected based on their relationship, and a flow diagram is presented according to the engagement situation of the adversary and ownship. Third, a behavior tree model is applied as a decision-making model to implement the flow diagram as a simulation program. The behavior tree offers good scalability because nonleaf nodes can be added when sophisticated and complex decision-making is required. The proposed method has the advantage of making all maneuvers performed by the algorithm understandable and interpretable. Additionally, it can replace expensive and dangerous dogfighting training for student pilots because the proposed model can emulate maneuvers that manned pilots would perform. To verify the proposed method, the evaluation criteria from the AlphaDogfight Trials are equally applied in the simulation. The experimental results demonstrate that the proposed method has superior engagement capability as compared to the existing air-to-air combat models.

Bijections between the multifurcating unlabeled rooted trees and the positive integers

Colijn and Plazzotta (2018) [1] described a bijective scheme for associating the unlabeled bifurcating rooted trees with the positive integers. In mathematical and biological applications of unlabeled rooted trees, however, nodes of rooted trees are sometimes multifurcating rather than bifurcating. Building on the bijection between the unlabeled bifurcating rooted trees and the positive integers, we describe bijective schemes for associating the unlabeled multifurcating rooted trees with the positive integers. We devise bijections with the positive integers for a set of trees in which each non-leaf node has exactly k child nodes, and for a set of trees in which each non-leaf node has at most k child nodes. The calculations make use of Macaulay's binomial expansion formula. The generalization to multifurcating trees can assist with the use of unlabeled trees for applications in evolutionary biology, such as the measurement of phylogenetic patterns of genetic lineages in pathogens.

Accelerated and Interpretable Oblique Random Survival Forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles have high prediction accuracy, but assessing many linear combinations of predictors induces high computational overhead. In addition, few methods have been developed for estimation of variable importance (VI) with oblique RSFs. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate VI with the oblique RSF. Our computational approach uses Newton-Raphson scoring in each non-leaf node, We estimate VI by negating each coefficient used for a given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In benchmarking experiments, we find our implementation of the oblique RSF is hundreds of times faster, with equivalent prediction accuracy, compared to existing software for oblique RSFs. We find in simulation studies that “negation VI” discriminates between relevant and irrelevant numeric predictors more accurately than permutation VI, Shapley VI, and a technique to measure VI using analysis of variance. All oblique RSF methods in the current study are available in the aorsf R package, and additional supplemental materials are available online.

Hypotheses Tree Building for One-Shot Temporal Sentence Localization

Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on dense video frame annotations, which require a tremendous amount of human effort to collect. In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. Particularly, we propose an effective and novel tree-structure baseline for one-shot TSL, called Multiple Hypotheses Segment Tree (MHST), to capture the query-aware discriminative frame-wise information under the insufficient annotations. Each video frame is taken as the leaf-node, and the adjacent frames sharing the same visual-linguistic semantics will be merged into the upper non-leaf node for tree building. At last, each root node is an individual segment hypothesis containing the consecutive frames of its leaf-nodes. During the tree construction, we also introduce a pruning strategy to eliminate the interference of query-irrelevant nodes. With our designed self-supervised loss functions, our MHST is able to generate high-quality segment hypotheses for ranking and selection with the query. Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods.

An energy-efficient command broadcast protocol in dynamic WSNs

In wireless sensor networks (WSNs), a sink must broadcast periodically a command message to a group of nodes or all nodes to control the operation of the network. However, the sparse distribution and limited resources of nodes bring difficulties for efficient broadcasting. In this paper, we propose an energy-efficient command broadcast protocol based on the construction of a spanning tree which includes maximizing the number of leaf nodes, and the allocation of one sharable slot to each tree level. The sharable slot that allocates to tree level will be divided into multiple mini slots, and non-leaf nodes at the same level will compete for broadcasting; in this way, collision can be reduced. Moreover, a node that moves to another level can use the sharable slot allocated to the new level without reallocating the slots. Extensive simulations prove that this approach not only achieves high reliability of message delivery but also energy efficiency.

A fast and scalable method for inferring phylogenetic networks from trees by aligning lineage taxon strings.

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, and then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature, and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every nonleaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on average.

Tree evolution processes for bucket increasing trees

Bucket increasing trees are multilabelled generalizations of increasing trees, where each non-leaf node carries b labels, with b≥1 a fixed integer. We provide a fundamental result, giving a complete characterization of all families of bucket increasing trees that can be generated by a tree evolution process. We also provide several equivalent properties, complementing and extending earlier results for ordinary increasing trees to bucket trees. Additionally, we state second order results for the number of descendants of label j, again extending earlier results in the literature.

COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

For energy constraint wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding, which allows a set of nodes (called concurrent senders ) to immediately broadcast the received packet without any backoff, is a promising approach to improve the flooding speed. We observe that selecting either large or small number of concurrent senders cannot achieve the optimal flooding speed in different deployments. There is a tradeoff between the degradation of concurrent broadcast efficiency and the missing of early receiving chance. In this article, we propose COFlood (Concurrent Opportunistic Flooding), a practical and efficient concurrent flooding protocol in asynchronous duty cycle networks. First, COFlood constructs a concurrent flooding tree in distributed manner. The non-leaf nodes are selected as concurrent senders and they can cover the entire network while reserving the most capacity of concurrent broadcast for later added opportunistic concurrent senders. Moreover, we find that exploiting both early wake-up nodes and long lossy links can speed up the concurrent flooding tree-based network flooding by increasing the early receiving chances. Then, COFlood develops a lightweight method to select the nodes that meet the conditions of these two opportunities as opportunistic concurrent senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

Mining Top-k Frequent Patterns in Large Geosocial Networks: A Mnie-Based Extension Approach

Frequent pattern mining (FPM) has played an important role in many graph domains, such as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of social network augmented by geographical information. However, in addition to the exponential time complexity of the problem, we face the challenge of efficient subgraph retrieval since we are interested in patterns in a specific region in such a network. For this reason, we formulate the top-k FPM problem in large geo-social networks. Specifically, we devise a novel framework for subgraph retrieval and FPM mining with a series of optimizations. First, we propose a neighboring-aware R-tree (NaR-Tree) index structure to alleviate the challenge of retrieving subgraphs from a large graph. NaR-Tree is a variant of R-tree in which each nonleaf tree node further maintains some edge statistics information for the rectangle related to it. Second, we define the concept of minimum image-based support of edges (MNIE). With the help of the NaR-Tree and MNIE-based pattern extension approach, a mining algorithm that addresses the problem of exponential candidate patterns is proposed. We also present a lazy retrieval strategy to reduce the frequency of subgraph retrieval. Finally, we adopt an edge sampling approach to further accelerate the mining process. Extensive experiments on real-world and synthesized datasets are conducted to demonstrate the effectiveness and efficiency of our solution.

Hierarchical Activity Recognition Based on Belief Functions Theory in Body Sensor Networks

Wearable human activity recognition has raised great interest among researchers in the field of Body Sensor Networks (BSNs). However, the classification of human complex activities, especially those similar activities such as walking upstairs and downstairs, is very challenging for the design of efficient activity recognition models. Considering that the hierarchical data structure is helpful to improve the performance of classifiers in effectively detecting the inter-class differences, here, we propose a novel hierarchical activity recognition method based on Belief Functions (BFs) theory. Specifically, in the training stage, we first use Long-Short Term Model (LSTM) to classify the predefined activities and then determine the similarities between each pair of activities through a confusion matrix. Based on this, the activities can be organized into a tree-based hierarchical structure through hierarchical clustering and then the Extreme Learning Machine (ELM) model for each nonleaf node in hierarchical structure can be trained, respectively. In the testing stage, we also propose a novel fast tree-based hierarchical combination rule to fuse the outputs of all trained ELMs. Finally, the proposed hierarchical activity recognition is evaluated by using two widely used UCI datasets: Smartphone and mHealth. The experimental results show that the proposed method is more accurate than the state-of-art methods.

Oblique and rotation double random forest

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.

Regularized regression on compositional trees with application to MRI analysis.

A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non-leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation-free tree-based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analyzing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory decline and volume of brain regions that are consistent with current understanding.

Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily identifies the changes made in the data due to proper structure. The developed model supports privacy-preserving public auditing to provide a secure cloud storage system. The data owners upload the data in the cloud and edit the data using the private key. An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches. The data files requested by the data owner are audit by a third-party auditor and the multi-owner authentication method is applied during the modification process to authenticate the user. The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.

Tag-Enhanced Dynamic Compositional Neural Network over arbitrary tree structure for sentence representation

Learning the distributed representation of a sentence is a fundamental operation for a variety of natural language processing tasks, such as text classification, machine translation, and text semantic matching. Tree-structured dynamic compositional networks have achieved promising performance in sentence representation due to its ability in capturing the richness of compositionality. However, existing dynamic compositional networks are mostly based on binarized constituency trees which cannot represent the inherent structural information of sentences effectively. Moreover, syntactic tag information, which is demonstrated to be useful in sentence representation, has been rarely exploited in existing dynamic compositional models. In this paper, a novel LSTM structure, ARTree-LSTM, is proposed to handle general constituency trees in which each non-leaf node can have any number of child nodes. Based on ARTree-LSTM, a novel network model, Tag-Enhanced Dynamic Compositional Neural Network (TE-DCNN), is proposed for sentence representation learning, which contains two ARTree-LSTMs, i.e. tag-level ARTree-LSTM and word-level ARTree-LSTM. The tag-level ARTree-LSTM guides the word-level ARTree-LSTM in conducting dynamic composition. Extensive experiments demonstrate that the proposed TE-DCNN achieves state-of-the-art performance on text classification and text semantic matching tasks.

Automatic QoS-aware web services composition based on set-cover problem

By definition, web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. In this article, the formulation of the automatic web-services composition is proposed as several set-cover problems and an approximation algorithm has been exploited to solve them. In proposed method, the web-service composition has been carried out within two main phases, the top-down expansion of the composition tree, and the production of composed service by bottom-up traversal of composition tree. In the first phase, the production of a composition tree (similar to the production of tree in problemsolving by searching) is proposed by starting from the output or post-conditions of the requested service towards its input or pre-conditions. Each node or state of the tree is a set of inputs and/or outputs or conditions, and services as tree edges illustrate the transition from one node to another. In the second phase, finding the path from the leaves of the produced composition tree to the root is considered equal to reaching the output of requested service, and this path specifies the involved services and the composition plan. The requested service input set determines the available leaves of the composition tree. To achieve each non-leaf node of the tree, a set-cover problem is produced and solved using a greedy approximation algorithm. If the production and solving of the set-cover problems continues hierarchically until it reaches the root node, the composition plan and cost of the required composition service will be specified. The main focus of this research is the joint sequential and parallel composition with the aim of producing near-optimal and QoS-aware composed services.

Linear matching method of database index files based on discrete mathematics

A linear matching method for database index files based on discrete mathematics is proposed. Firstly, the relaxation clustering algorithm is adaptively improved. The corresponding Gauss kernel parameters are obtained by using the domain information of data points direction of the cluster load. The cluster minimal circumscribed matrix is set up according to the calculated coordinate component value, and the optimal cluster is obtained. Electronic dictionary is used to calculate the semantic association between tags of index files after clustering to obtain the name similarity, the cardinal similarity is obtained according to the cardinal constraints, the context similarity is calculated through the path between leaf node and root node, the matched descendant node is queried by the neighbor of non-leaf node, and the descendant node is queried. The number of leaf nodes and their similarity to the original node are calculated for different paths to complete the matching of database index files. The experimental results show that the average energy consumption of matching index files is 439 J and the matching accuracy is 99.72%.

Automated Cytomegalovirus Retinitis Screening in Fundus Images.

This work proposes an automated algorithms for classifying retinal fundus images as cytomegalovirus retinitis (CMVR), normal, and other diseases. Adaptive wavelet packet transform (AWPT) was used to extract features. The retinal fundus images were transformed using a 4-level Haar wavelet packet (WP) transform. The first two best trees were obtained using Shannon and log energy entropy, while the third best tree was obtained using the Daubechies-4 mother wavelet with Shannon entropy. The coefficients of each node were extracted, where the feature value of each leaf node of the best tree was the average of the WP coefficients in that node, while those of other non-leaf nodes were set to zero. The feature vector was classified using an artificial neural network (ANN). The effectiveness of the algorithm was evaluated using ten-fold cross-validation over a dataset consisting of 1,011 images (310 CMVR, 240 normal, and 461 other diseases). In testing, a dataset consisting of 101 images (31 CMVR, 24 normal, and 46 other diseases), the AWPT-based ANN had sensitivities of 90.32%, 83.33%, and 91.30% and specificities of 95.71%, 94.81%, and 92.73%. In conclusion, the proposed algorithm has promising potential in CMVR screening, for which the AWPT-based ANN is applicable with scarce data and limited resources.