Nodes Of Tree Research Articles

A comprehensive framework for quantitative risk assessment of organizational networks using FAIR-modified attack trees

Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.

Benchmarking performance through efficiency analysis trees: Improvement strategies for colombian higher education institutions

We introduce benchmarking analysis based on state-of-the-art machine learning techniques applied to the measurement of efficiency to assess the performance of Higher Education Institutions (HEIs). We rely on Efficiency Analysis Trees (EAT) and its Convexified frontier counterpart (CEAT) to assess the efficiency of 144 private HEIs in Colombia and compare the results with those achieved with classical Data Envelopment Analysis (DEA). Both EAT and CEAT show a higher discriminatory power than DEA when determining efficiency scores. Our results identify the different splits of the production frontier, corresponding to each node of the efficiency tree, which groups HEIs according to specific management models. By identifying relevant peers for inefficient observations at the node level, we show which strategic guidelines can be adopted to improve the performance of each HEI. This process encourages mutual learning and suggests potential changes within each node leading to efficiency improvements.

On impurity functions in decision trees

Impurity functions are crucial in decision trees. These functions help determine the impurity level of a node in a decision tree, guiding the splitting criteria. However, two primary ambiguities have surrounded impurity functions: (1) the question of their non negativity and (2) the debate over their concavity. In this paper, we address these uncertainties by delving into the characteristics of impurity functions. We establish that the non negativity of an impurity function is inconsequential. Through counter examples, we disprove the equivalence between an impurity function and a concave function. We identify an impurity function that is not concave and a concave function that is not an impurity function. Interestingly, we find an impurity function that results in a negative impurity reduction. Furthermore, we validate several significant properties of impurity functions. For example, we demonstrate that when an impurity function is concave, the impurity reduction remains nonnegative for multiway divisions. We also discuss the sufficient conditions for a concave function to be an impurity function. Our numerical results further indicate that a positive linear combination of the two most popular impurity functions, namely Gini Index and Entropy, may surpass the individual performance of each when applied to the well-known German credit dataset.

An efficient failure-resilient mutual exclusion algorithm for distributed systems leveraging a novel zero-message overlay structure

In this paper, we provide a novel failure-resilient token-based mutual exclusion (ME) algorithm for distributed systems. Like a few other solutions in the literature, the proposed solution leverages a logical tree as its underlying topology. However, unlike any other solution, the tree is built using a probabilistic, fully distributed approach, without exchanging messages. The current tree-based ME algorithms often overlook considerations for node/link failures or offer costly methods for failure recovery. The proposed algorithm overcomes these limitations by providing an effective solution to maintain a logarithmic cost in case of node failures. The overlay structure – a unique logical tree – used to control access to the critical section maintains its consistency even when nodes fail. An extensive simulation study demonstrates the viability and efficiency of the proposed algorithm under various node failure models, and relevant metrics (e.g., node queue dimension, number of exchanged messages, and number of disconnected nodes) indicate a graceful degradation in performance with decreasing number of functioning nodes. For instance, for 4,096 nodes organized in a logical tree of arity 4 and with 4 physical nodes for logical node, a negligible number of nodes is disconnected from the tree after 250 epochs when the per-node per-epoch failure probability is pf≤0.0008. With a pf=0.0016, less than 10% of the nodes are disconnected. The proposed algorithm avoids node disconnection while minimally impacting the load of the logical tree nodes. In addition, for the same architecture, when both tree arity and cardinality are 4, after 250 epochs, the node load has demonstrated minimal variation and remains in close proximity to the original load. Moreover, experimental results also reveal a graceful degradation of algorithm performance. The fully distributed solution, rich parametrization for different trade-offs, and viability and resilience of the proposed algorithm also pave the way for future research.

An Algorithm for Finding Optimal k-Core in Attribute Networks

As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.

Decision Tree for 305-Day Milk Yield in Cross-bred Cattle

Cows' Lactation Milk Yield (LMY) is a crucial factor in animal breeding operations. Investigating the influence of potential environmental factors on lactation milk yield is of paramount importance and in order to identify the various factors influencing lactation milk yield, dairy cattle records were analysed using the regression tree approach. Age, Parity (P), Lactation Length (LL), and Calving Season (CS) were taken into account as explanatory variables while 305-day Milk Yield (MY) as a dependent variable. Decision tree study revealed that Lactation Length, followed by Parity, Age, and Calving Season, had the greatest impact on the 305-d milk output of cross-bred cows. The regression trees use the tree to represent the recursive part. Each terminal node or leaf of the tree represents a cell of the section and has just added a simple pattern applied to it in this cell. It was evident from nodes (branches) in regression tree, that cows with parities of 1 and 4 (node 11) produced less milk than cows with parities of 2, 3, 5, 6, 7 and 8 (node 10). More milk was produced by cows older than 4.3 years and whose calving seasons were spring and summer (node 40). With the use of the regression tree method, we were able to extract sub-homogenous groups based on the explanatory variables from records of cross-bred cattle and determine the combinations of environmental conditions that produced the maximum 305-d milk yield.

Eigenstate calculation in the state-averaged (multi-layer) multi-configurational time-dependent Hartree approach.

A new approach for the calculation of eigenstates with the state-averaged (multi-layer) multi-configurational time-dependent Hartree (MCTDH) approach is presented. The approach is inspired by the recent work of Larsson [J. Chem. Phys. 151, 204102 (2019)]. It employs local optimization of the basis sets at each node of the multi-layer MCTDH tree and successive downward and upward sweeps to obtain a globally converged result. At the top node, the Hamiltonian represented in the basis of the single-particle functions (SPFs) of the first layer is diagonalized. Here p wavefunctions corresponding to the p lowest eigenvalues are computed by a block Lanczos approach. At all other nodes, a non-linear operator consisting of the respective mean-field Hamiltonian matrix and a projector onto the space spanned by the respective SPFs is considered. Here, the eigenstate corresponding to the lowest eigenvalue is computed using a short iterative Lanczos scheme. Two different examples are studied to illustrate the new approach: the calculation of the vibrational states of methyl and acetonitrile. The calculations for methyl employ the single-layer MCTDH approach, a general potential energy surface, and the correlation discrete variable representation. A five-layer MCTDH representation and a sum of product-type Hamiltonian are used in the acetonitrile calculations. Very fast convergence and order of magnitude reductions in the numerical effort compared to the previously used block relaxation scheme are found. Furthermore, a detailed comparison with the results of Avila and Carrington [J. Chem. Phys. 134, 054126 (2011)] for acetonitrile highlights the potential problems of convergence tests for high-dimensional systems.

Intelligent path planning by an improved RRT algorithm with dual grid map

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

On the protected nodes in exponential recursive trees

The exponential recursive trees model several kinds of networks. At each step of growing of these trees, each node independently attracts a new node with probability p, or fails to do with probability 1 − p. Here, we investigate the number of protected nodes, total path length of protected nodes, and a mean study of the protected node profile of such trees.

Subspace learning machine (SLM): Methodology and performance evaluation

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it uses probabilistic projections of features in S0 to yield 1D subspaces and finds the optimal partition for each of them. This is equivalent to partitioning S0 with hyperplanes. A criterion is developed to choose the best q partitions that yield 2q partitioned subspaces among them. We assign S0 to the root node of a decision tree and the intersections of 2q subspaces to its child nodes of depth one. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops and each leaf node makes a prediction. The idea can be generalized to regression, leading to the subspace learning regressor (SLR). Furthermore, ensembles of SLM/SLR trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM/SLR trees, ensembles and classical classifiers/regressors.

CONCEPT OF BUILDING A LIBRARY OF TASKS AND SOLUTIONS

The article develops the concept of building a library of tasks and their solutions in the form of computational processes (CPs) and Petri nets (PNs) with the preliminary transformation of CPs into PNs. The conversion includes the extraction of macro operations (MOs) from CPs as a result of their analysis, the construction of PNs and their fragments as models of CPs and MOs, and the modeling of PNs and their fragments. The architecture, structure and interface of the library are proposed. Computational process is a sequence of time-ordered operations and procedures of varying degrees of complexity. The set of PNs, as well as the set of problems for which PNs are solutions, is divided into classes and subclasses. A class includes problems such as Combinatorial, Linear Algebraic Equations, etc.; the Combinatorial class includes subclasses of problems such as Sorting, Search, Sets, Graphs, etc. A macro operation is a fragment of a GP that is a functionally complete computational construct. MOs can have different levels of complexity, starting with the elementary one; they can be built hierarchically. A Petri net is a bipartite graph with two interconnected objects: event-condition, process-resource, etc. Fragments of the PN are models of the MO. The components of the library are tree nodes supplemented with files of their verbal descriptions built according to certain rules. The architecture, structure, interface, and operations of the library provide convenient management, editing, and expansion of knowledge about computational processes and macro operations, making the library open for further research and development by the developer. All files are available to the user, but without the right to edit. The paper presents an example of the initial filling of the library for the Sorting task.

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems.

Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke

ObjectiveTo construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML). MethodsRetrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The “glmnet”, “rpart”, “xgboost”, “randomForest”, “neuralnet” packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance. ResultsThe post-clustering dataset contains 139 patients (group W) and 122 patients (group P). The neutrophil multiplied by D-dimer (NDM) has predictive value in all ML prediction models in this study. In the decision tree model, NDMQ occupies the first tree node, When NDM≤5.62 and the age<74.5, the probability of poor prognosis of AIS is less than 20 %. When NDM>5.62 and accompanied by pneumonia, the incidence of poor prognosis of AIS is about 90 %. In the Random Forest (RF) model, NDMQ had the highest Gini index. The variable combination screened by the RF model had the best performance in the neural network, and the accuracy, sensitivity, specificity, PPV, and NPV of the external validation were 0.800, 0.774, 0.833, 0.857, and 0.741, respectively. The RF model had the best performance in the external clinical validation datasets, with accuracies of 0.646, 0.697, 0.695, and 0.713, respectively. ConclusionsNDM shows predictive value for AIS short-term prognosis in all ML models in this study. The optimal model in screening characteristic variables and the performance of in external clinical datasets was RF model. In the analysis of medical data with small sample size and outcome as categorical variables, RF could be used as the main algorithm to build a model.

Intelligent financial management system based on Data Mining Technology

This article is based on decision tree algorithm and data mining analysis technology to achieve the intelligence of financial management and decision-making in enterprise, providing effective support for financial management. An improved metric based C4.5 decision tree algorithm was proposed, which combines data warehousing, data mining, and analysis techniques, and adopts a data-driven approach to establish intelligent financial management in enterprise. Apply it to early warning of budget execution progress in financial projects, conduct experimental analysis and result research. In this experiment, data mining achieved significant results. From the root node of the decision tree, it can be seen that whether the project budget execution progress at the end of July is greater than 22.79% is of great significance for determining whether the project budget can reach 97% of the year-end budget execution progress. Based on the experimental results, it is concluded that as of July, more attention should be paid to the execution of project budgets, and effective management of data is the primary task and goal of financial institutions in enterprise.

FR-CSG: Fast and Reliable Modeling for Constructive Solid Geometry.

Reconstructing CSG trees from CAD models is a critical subject in reverse engineering. While there have been notable advancements in CSG reconstruction, challenges persist in capturing geometric details and achieving efficiency. Additionally, since non-axis-aligned volumetric primitives cannot maintain coplanar characteristics due to discretization errors, existing Boolean operations often lead to zero-volume surfaces and suffer from topological errors during the CSG modeling process. To address these issues, we propose a novel workflow to achieve fast CSG reconstruction and reliable forward modeling. First, we employ feature removal and model subdivision techniques to decompose models into sub-components. This significantly expedites the reconstruction by simplifying the complexity of the models. Then, we introduce a more reasonable method for primitive generation and filtering, and utilize a size-related optimization approach to reconstruct CSG trees. By re-adding features as additional nodes in the CSG trees, our method not only preserves intricate details but also ensures the conciseness, semantic integrity, and editability of the resulting CSG tree. Finally, we develop a coplanar primitive discretization method that represents primitives as large planes and extracts the original triangles after intersection. We extend the classification of triangles and incorporate a coplanar-aware Boolean tree assessment technique, allowing us to achieve manifold and watertight modeling results without zero-volume surfaces, even in extreme degenerate cases. We demonstrate the superiority of our method over state-of-the-art approaches. Moreover, the reconstructed CSG trees generated by our method contain extensive semantic information, enabling diverse model editing tasks.

Research on real-time data analysis system of sports training based on reconfigurable data mining technology

Abstract This paper utilizes smart wearable devices to quickly capture, record, and store performance information (calorie consumption assessment, pedometer, and heart rate monitoring), location information, and biochemical data of sports training populations through the sensory layer. Based on the reconfigurable data mining-related technology, combined with the data and test scores of the sports training population, data mining analysis is carried out to form valuable physiological, physical, and other characteristic information, which provides a scientific and referable strategy for sports training, thus also achieving the purpose of improving the training effect of athletes. The results show that, from the visualization and analysis results of the decision tree model for boys and girls, the girls’ long-distance running event (800 m) and boys’ pull-ups are located in the root node of the decision tree, which has the most significant effect on the performance level. Consequently, sports training programs were created that were more logically based on the distinction between male and female students. In individual exercise energy expenditure, by 420s, the limiting energy expenditure rate of 5.6 kcal/30s exceeded that of the athletes. Moreover, with time, energy expenditure continues to decline. The accuracy of this test can be 90%.

MPTree: A Sampling-based Vehicle Motion Planner for Real-time Obstacle Avoidance

This paper presents a novel and modular framework, named MPTree, for real-time vehicle motion planning with dynamic obstacle avoidance. MPTree adopts a sampling-based algorithm, to explore a tree of Motion Primitives (MPs) and return near-optimal trajectories. Specifically, MPTree builds motion primitives to connect the tree nodes (waypoints), sampled in a mesh of waylines on the local planning horizon. The tree exploration is based on a semi-structured RRTa, with an application-specific cost function (e.g., minimum-jerk or minimum-time) and high-level behavioral policy. We show examples of MPTREE’s specialization for urban environments and autonomous racing, using fast-to-evaluate motion primitives to accelerate the tree exploration phase. A prototype implementation is tested in a closed-loop simulation environment. Our preliminary results show that MPTree provides feasible collision-free trajectories while ensuring low computational times.

A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

The Syntax of Prominence

The standard view on discourse pronoun resolution is that determining the antecedents of discourse pronouns is typically a function of extralinguistic reasoning. In contrast, Stojnić (2021) argues that pronoun resolution is a function of linguistic facts. In this article we offer what we take to be a friendly amendment to the technical aspects of Stojnić’s proposal. Our point of departure will be with our idea that prominence is not determined by the position of the candidate antecedent within a stack, but rather by its position within standard syntactic tree structures, extended to include discourse-level trees. Our proposal leans on the notion of p-scope, a proof-theoretic accessibility relation among tree nodes which we develop in Ludlow and Živanović (2022), and the notion of closeness built on standard accounts of syntactic locality. The key idea is that a pronoun’s antecedent resolves to its closest p-scoper; specifically, p-scope determines the potential antecedents, and the closeness relation orders these by prominence. Coherence relations, which we provisionally represent as syntactic heads, can be then seen as affecting accessibility and prominence indirectly, in virtue of their position in traditional LF tree structures.

AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILES

The exponential growth of bioinformatics in the healthcare domain has revolutionized our understanding of DNA, proteins, and other biomolecular entities. This remarkable progress has generated an overwhelming volume of data, necessitating big data technologies for efficient storage and indexing. While big data technologies like Hadoop offer substantial support for big XML file storage, the challenges of indexing data sizes and XPath query performance persist. To enhance the efficiency of XPath queries and address the data size problem, a novel approach that is derived from the spatial indexing method of the R-tre family. The proposed method is to modify the structure of leaf nodes in the indexing tree to preserve XML-sibling connections. Then, new algorithms for constructing the new tree structure and processing sibling queries better are introduced. Experimental results demonstrate the superior efficiency of sibling XPath queries with reduced data sizes for indexing, while other XPath queries exhibit notable performance improvements. This research contributes to the development of more effective indexing methods for managing and querying large XML datasets in bioinformatics applications, ultimately advancing biomedical research and healthcare initiatives.