Nodes Of Tree Research Articles

A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking

Many applications suitable for multicast transmission, such as video conferencing and live e-commerce, demand high Quality of Service (QoS) and require data delivery to be completed within specified delay constraints. Some methods have been proposed for constructing delay-constrained multicast routing based on network state. However, obtaining precise network latency can be challenging, resulting in inaccuracies in delay-constrained routing calculations and, ultimately, the inability to meet application requirements. Additionally, many methods engage in an indiscriminate exploration of potential paths in the network, causing significant message processing overhead. This paper proposes an Information-Centric Networking (ICN)-based approach for delay-constrained multicast routing. Our method dynamically constructs multicast paths from tree nodes to receivers based on real-time path status detection during the join message propagation phase. Additionally, we present a method for acquiring neighborhood state information to facilitate real-time routing decisions. To curtail indiscriminate path exploration, our approach uses the ICN Name Resolution System (NRS) to obtain and select potential optimal tree nodes. For this purpose, we design a multicast service registration and resolution mechanism using the ICN Name Resolution System (NRS). Simulation results indicate that our approach exhibits a higher success ratio and concurrently incurs lower message processing overhead than some other methods, particularly in situations with stringent delay constraints.

Adaptive in-memory representation of decision trees for GPU-accelerated evolutionary induction

Decision trees (DTs) are a type of machine learning technique used for classification and regression problems. They are considered to be a part of explainable artificial intelligence due to their transparent and interpretable nature. The traditional approach in building DTs involves a top-down greedy search, which is usually fast but may result in a sub-optimal solution. An alternative method is evolutionary induction. It allows for more global exploration and can lead to simpler DTs, but it is computationally more expensive. To speed up the induction, different parallel approaches have been considered, with the GPU-based ones being the most efficient.In this paper, we extend the GPU-supported algorithm of DT evolutionary induction to use less memory and time resources. An improved in-memory representation of DTs is introduced, which adapts depending on the tree structure. Two types are possible: compact and complete representations. A compact one is used for DTs that are sparsely filled. It contains only existing tree nodes and therefore the links between parent and child nodes must be explicitly stored (along with the data). On the other hand, a complete representation is applied when the tree levels are densely filled. Then, placeholders for all potential nodes are reserved and the parent position can be easily calculated based on the child’s place (and vice versa). The algorithm switches between the representations based on a density factor of tree filling.To validate our concept, we conducted experiments on both real-life and artificial datasets with varying sizes and dimensions. The results show that the DT in-memory representation affects the time and memory requirements a lot. The most efficient is the adaptive one. It not only speeds up the induction time but also reduces memory requirements (in terms of both transfer and occupancy). As a result, the evolutionary induction of DTs is closer to being competitive with state-of-the-art greedy inducers in terms of computation time.

Elastic Monte Carlo Tree Search

Strategy games are a challenge for the design of AI agents due to their complexity and the combinatorial search space they produce. State abstraction has been applied in different domains to shrink the search space. Automatic state abstraction methods have gained much success in the planning domain and their transfer to strategy games raises a question of scalability. In this paper, we propose Elastic MCTS, an algorithm that uses automatic state abstraction to play strategy games. In Elastic MCTS, tree nodes are clustered dynamically. First, nodes are grouped by state abstraction for efficient exploration, to later be separated for refining exploitable action sequences. Such an elastic tree benefits from efficient information sharing while avoiding using an imperfect state abstraction during the whole search process. We provide empirical analyses of the proposed method in three strategy games of different complexity. Our empirical results show that in all games, Elastic MCTS outperforms MCTS baselines by a large margin, with a considerable search tree size reduction at the expense of small computation time. The code for reproducing the reported results can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GAIGResearch/Stratega</uri> .

Прогнозирование риска неблагоприятного исхода при COVID‑19 с помощью метода деревьев классификации с учетом возраста и количества коморбидной патологии по данным инфекционного госпиталя.

Objective: to determine the probability of the risk of an unfavorable outcome using the classification tree method in patients with COVID‑19 who were treated in an infectious hospital based on the analysis of the contribution of such predictors as age and the amount of comorbid pathology. Materials and methods. The data of outpatient records of 5304 patients who were treated in an infectious diseases hospital with a diagnosis of COVID‑19 from January 1, 2021 to January 1, 2022 were analyzed. The age of the examined patients was 62 [56–66] years. Among 5,304 patients, there were 2,891 males (54,5%) and 2,413 females (45,5%). The patients were divided into age groups according to the WHO classification. The frequency of comorbid pathology was analyzed taking into account the nosological unit of the disease registered in at least 1% of the patients included in the study. Results. In the studied sample, the elderly prevailed in a larger percentage – 46.8%. The presence of one or more comorbid pathology was revealed in the prevailing number of hospitalized – in 5,244 people (98,9%). The most common comorbid pathology in the examined patients was arterial hypertension in 2038 people (38,4%), coronary heart disease in 1997 people (37,7%) and type 2 diabetes mellitus in 1629 people (30,7%). A classification tree was constructed to predict the risk of the probability of an unfavorable outcome in patients with COVID‑19.The minimum number of observations in the parent node in the classification tree was 400 people, in the child node – 200 people. In the resulting classification tree, 8 terminal nodes were observed. Conclusion. The probability of the risk of an unfavorable outcome in the analyzed sample of patients increases with an increase in the number of comorbid pathology and the age of patients. According to the forecast using the classification tree method, the greatest probability of risk (3,2 times) of an unfavorable outcome in relation to the general sample was among elderly persons+centenarians with more than three comorbid pathology. The developed classification tree showed a high probability of correct predictions (80%). The sensitivity of the resulting model was 77,9%, the specificity was 64,2%.

An Adaptive Metadata Management Scheme Based on Deep Reinforcement Learning for Large-Scale Distributed File Systems

A major challenge confronting today’s distributed metadata management schemes is how to meet the dynamic requirements of various applications through effectively mapping and migrating metadata nodes to different metadata servers (MDS’s). Most of the existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make a timely and efficient update on the distribution of nodes. In this paper, we present the first deep reinforcement learning-leveraged distributed metadata management scheme, AdaM, to address the aforementioned dilemma. AdaM is an adaptive fine-grained metadata management scheme that trains an actor-critic network to migrate “hot” metadata nodes to different MDS’s based on its observations of the current “states” (i.e., access pattern, the structure of namespace tree and current distribution of nodes on MDS’s). Adaptive to varying access patterns, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. Besides, we propose a self-adaptive metadata cache policy, which dynamically combines the two strategies of managing caches on the server side and the client side to gain better query performance. Last but not least, we design a distributed metadata processing 2PC Protocol called MST-based 2PC to ensure data consistency. Experiments on a real-world dataset demonstrate the superiority of our proposed method over other schemes.

Multiresolution Representation of Geoelectric Model and Parameter Operation in Wavelet Domain

In this paper, an algorithm for wavelet domain geoelectric model representation and parameter manipulation is introduced, which can be utilized for multiresolution inversions of geophysical data based on tree structure. With proper wavelet choice, the model can be represented by much less parameters than traditional model representation, expecting to reduce the computational cost for inversion remarkably. The use of tree structure to represent the wavelet coefficients makes the parameter operations (e.g., perturbating model parameter and adding more details in a model) in inversion much simpler. Introducing detail in a model or removing detail from a model can be simply realized by adding nodes to or deleting nodes from the existing tree structure. The model parameter value can be perturbated by changing the tree node values. Our algorithm is general and can be applied to develop any geophysical inversions in wavelet domain with trivial additional work once the forward modeling and inversion algorithm is available.

IME: Efficient list-based method for incremental mining of maximal erasable patterns

Erasable pattern mining can help factories facing a financial crisis increase productivity by identifying and eliminating unprofitable products. The Flag-GenMax-EI algorithm extracts Maximal Erasable Itemsets (MEIs); however, it does not support dynamic data. In practice, many applications create databases incrementally. Using the Flag-GenMax-EI algorithm to mine maximal erasable patterns from incremental databases is clearly very costly because it must be run each time. In this paper, an efficient method called IME is proposed for incremental mining of maximal erasable patterns. IMEI-List and IMEP-List are two new data structures introduced by the proposed method. These lists allow the algorithm to update all tree nodes without rescanning the updated database (original database + new database) and recreating the nodes. This is the first study of incremental mining of maximal erasable patterns. Extensive experimental results on dense and sparse incremental data show that the proposed algorithm improves scalability. It extracts MEIs much faster than the Flag-GenMax-EI algorithm in different modes of database update.

Design of Monitoring Tools for Data Centre Downtime Reduction

This paper presents a new monitoring tool and event management method for data centre compute, network and storage infrastructure based on node event processing. The uptime of highly classified data centres are not only to be maintained at the highest level of reliability and availability of the operation, but also fast, specific event identification and rectification, which altogether improves availability of the resources is important. The new method, using a tree node for each element of the data centre resources provides information about the compute, network and storage file system configuration in a specific node. Its major advantage is that in our case where a large number of heterogeneous computers are present, it helps us in monitoring all the elements of the computer resources and gives information for alerting the associated work centres before any of the identified events that might occur. By monitoring and informing apriori to the concerned work centres the state of the systems, it lowers errors in data centre physical infrastructure operating costs, improving at the same time the level of operations efficiency. This method resulted that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data centre physical infrastructure. In this paper, the design and development of two customised dashboards to monitor the compute, storage and network elements of the heterogeneous data centre for uptime maintenance and optimal performance is presented. The dashboards are designed, keeping in view the nature of tasks carried out and the resource requirements of various work centres in the data centre. One dashboard displays dynamically created icons for each of the compute resources in the data centre. On clicking any of the icon, complete details of the corresponding server is fetched showing the status, usage, configuration and available resources. Furthermore, a unique colouring scheme is followed wherein the icon is displayed green if the server is healthy and orange if the server is facing a resource crunch (disk, memory, etc.) and red if the server is not reachable. The dashboard GUI refreshes every 5 min (is configurable), displaying the latest status details of the servers in the data centre. The second Dashboard is developed with the capability to monitor the storage, cloud and network infrastructure components. The dashboard collects data from different elements of the storage i.e. Meta Data Servers, Storage, Core and Edge switches etc. and processes the collected data to a customized format for display. It delivers details like availability of Storage Meta Data Servers, switches and file systems, disk space capacity monitoring, file system backup status, Monitoring of the hierarchical Storage including Tape Library and the availability of Production ESXi hosts cluster. The GUI is updated with new requirements to further fine-tune and reduce manual intervention for monitoring operations.

Branching the limits: Robust 3D tree reconstruction from incomplete laser point clouds

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method for fine-grained 3D tree model reconstruction from incomplete point clouds is proposed. This approach addresses the structural reconstruction of trees, tackling two main problems: reconstructing the main trunk and branches based on data completeness. Initially, a 3D morphological algorithm is employed to separate the main trunk and branches in the point cloud. Next, branch point clouds are node-aggregated using clustering, and the trunk’s skeleton point is computed using a multi-scale curve fitting method. To account for incomplete branch point clouds, the Alpha shape is calculated and used as a constraint for the growth model of the L-system, enabling the automatic generation of branch structures. Finally, a morphologically constrained multi-level trunk fusion method is utilized to achieve complete tree model reconstruction. To validate the effectiveness of this method, five trees with varying structures and levels of complexity were selected for structural reconstruction and compared with state-of-the-art (SOTA) methods. Experimental results evince that the method delineated in this study exhibits an aptitude for adeptly approximating tree nodes, even in scenarios characterized by incomplete point cloud data. This approach transcends the reconstruction accuracy manifested by SOTA algorithms specific to three-dimensional tree modeling, simultaneously demonstrating a pronounced resilience to noise and enhanced robustness.

Application of visual multimedia technology in air transportation information service system

Abstract This paper uses visual multimedia technology to establish a model for civil aviation transportation information services. A training subset is generated with a random forest, and the strength and relevance indicators of the random forest are examined. The generated decision tree is run on the corresponding out-of-bag data to evaluate its classification ability and obtain its weights. The leaf nodes of the decision tree are normalized to calculate the out-of-bag estimation of classification strength to realize the application of visual multimedia technology in the civil aviation transportation information service system. The system scores after application are all 8 and above, and the highest can reach 10. The quality of its service efficiency and level is improved, and passengers traveling by air are satisfied.

A lattice structure for ancestral configurations arising from the relationship between gene trees and species trees

To a given gene tree topology G and species tree topology S with leaves labeled bijectively from a fixed set X, one can associate a set of ancestral configurations, each of which encodes a set of gene lineages that can be found at a given node of the species tree. We introduce a lattice structure on ancestral configurations, studying the directed graphs that provide graphical representations of lattices of ancestral configurations. For a matching gene tree topology and species tree topology G=S, we present a method for defining the digraph of ancestral configurations from the tree topology by using iterated cartesian products of graphs. We show that a specific set of paths on the digraph of ancestral configurations is in bijection with the set of labeled histories — a well-known phylogenetic object that enumerates possible temporal orderings of the coalescences of a tree. For each of a series of tree families, we obtain closed-form expressions for the number of labeled histories by using this bijection to count paths on associated digraphs. Finally, we prove that our lattice construction extends to nonmatching tree pairs, and we use it to characterize pairs (G,S) having the maximal number of ancestral configurations for a fixed G. We discuss how the construction provides new methods for performing enumerations of combinatorial aspects of gene and species trees.

Transduction from trees to graphs through folding

We introduce a fold operation that realises a tree-to-graph transduction by merging selected nodes in the input tree to form a possibly cyclic output graph. The work is motivated by the increasing use of graph-based representations in semantic parsing. We show that a suitable class of graphs languages can be generated by applying the fold operation to regular unranked tree languages. We investigate two versions of the fold operation, one that preserves a depth-first ordering between the edges, and one that does not. Finally, we demonstrate that the time complexity for the associated non-uniform membership problem is solvable in polynomial time for the order-preserving version, and NP-complete for the order-cancelling one.

Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.

Improved artificial fish swarm based optimize rapidly-exploring random trees multi-robot exploration algorithm

To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot’s exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm.

MSSD: An Efficient Method for Constructing Accurate and Stable Phylogenetic Networks by Merging Subtrees of Equal Depth

Background:: Systematic phylogenetic networks are essential for studying the evolutionary relationships and diversity among species. These networks are particularly important for capturing non-tree-like processes resulting from reticulate evolutionary events. However, existing methods for constructing phylogenetic networks are influenced by the order of inputs. The different orders can lead to inconsistent experimental results. Moreover, constructing a network for large datasets is time-consuming and the network often does not include all of the input tree nodes. Aims: This paper aims to propose a novel method, called as MSSD, which can construct a phylogenetic network from gene trees by Merging Subtrees with the Same Depth in a bottom-up way. background: Phylogenetic trees can represent the evolutionary history of genes vertically. There is a difference between phylogenetic trees of different genes due to the reticulate evolution events of species. Phylogenetic networks can represent reticulate evolutionary processes and show the difference between rooted gene trees. Methods:: The MSSD first decomposes trees into subtrees based on depth. Then it merges subtrees with the same depth from 0 to the maximum depth. For all subtrees of one depth, it inserts each subtree into the current networks by means of identical subtrees. Results:: We test the MSSD on the simulated data and real data. The experimental results show that the networks constructed by the MSSD can represent all input trees and the MSSD is more stable than other methods. The MSSD can construct networks faster and the constructed networks have more similar information with the input trees than other methods. Conclusion:: MSSD is a powerful tool for studying the evolutionary relationships among species in biologyand is free available at https://github.com/xingjiajie2023/MSSD. conclusion: The MSSD can construct networks faster and the constructed networks have more similar information with the input trees than other methods.

Sampling-based time-optimal path parameterization with jerk constraints for robotic manipulation

In this paper, a sampling-based time-optimal path parameterization (S-TOPP) method is proposed to address time-optimal trajectory planning problems with bounded jerks. The key insight of S-TOPP is that a tree of feasible nodes connected by edges is established to find a time-optimal trajectory on the temporal dimension. The tree establishment process includes two major phases at each stage, namely sampling and one-step backtracking. In the sampling phase, a new sampling strategy integrating historical information is proposed to obtain superior samples whereby fewer samples can be controlled automatically, reducing the calculation loss. In one-step backtracking phase, a “lazy” strategy is used to lazily skip constraint-checking when evaluating local connections, enabling S-TOPP to avoid checking the vast majority of nodes that have no chance of being in an optimal trajectory. Simulations and real-world experiments validate the feasibility and practicability of S-TOPP. Results show that S-TOPP is an effective solution to jerk-bounded time-optimal trajectory planning, with features that are more in line with the needs of the practical tasks compared with other methods.

Factors influencing the quality of acupuncture clinical trials: a qualitative interview of stakeholders

ObjectiveTo investigate the influencing factors on the quality of acupuncture clinical trials from the stakeholders, and to provide references for improving the quality of acupuncture clinical trials.MethodsA qualitative study based on semi-structured interviews was performed. Experts, acupuncturists, editors, and patients were interviewed. The interview results were thematically analyzed from transcribed audio recordings.ResultsA total of 38 stakeholders were interviewed, including 12 experts, 14 acupuncturists, 2 editors, and 10 patients. There were 25 tree nodes and 106 sub-nodes, with 1141 reference points. The key factors influencing the quality of acupuncture clinical trials could be divided into five core theme frameworks: a) trial design, b) trial conduction, c) research results reporting and publication, d) research evidence dissemination, and e) research evidence transformation and application.ConclusionsThe results reveal that to improve the quality of acupuncture trials, it should consider each step of trial design, trial conduction, research results reporting and publication, research evidence dissemination, and research evidence transformation and application. A guideline for quality control of the whole process of acupuncture clinical trials is needed.

Machine learning applied to understand perceptions, habits and preferences of lamb meat consumers in the Brazilian semi-arid region

The objective of this study was to characterize the profile of the lamb consumer in the Brazilian semi-arid region using a machine learning approach. A total of 347 questionnaires with questions about socioeconomic aspects, consumption habits and preferences, meat preparation, and appreciation due to product quality were applied. A combination of cluster analysis (hierarchical and k-means), canonical discriminant analysis, neural network, and tree analysis were performed. The machine algorithms revealed three clusters: Traditional profile - Mature rural consumers, married or single, low level of education, low salary level, average consumption of meat, who do not pay for quality and do not know the benefits of lamb products; Emerging profile: Young men and women, single, with a high level of education, intermediate salary level, low consumption of meat, pay for quality, and do know the benefits of lamb meat; and Conventional profile: Mature urban consumers who live in the city, married and single, with a high level of education and salary, low consumption of meat, pay for quality and do not know the benefits of lamb meat The neural network confusion matrix for classifying consumers in the profile determined that 75.5% were classified in their group of origin, which validates the construction of the typology of consumers by the cluster analysis algorithms. Age was the main variable to segregate the nodes of the decision tree (P < 0.001): Node 1- < = 22; Node 2 > 22 < 29 and Node 3 > 29 years The use of a machine learning approach was able to reveal three types of consumers and defined patterns that could serve as strategies for increasing consumption and greater insertion of lamb in the meat market, as well as analyzing the perception of Brazilians in relation to the meat quality, where these findings may have implications for labelling schemes, marketing and sales strategies for cooked lamb products.

Poly-ABE: A traceable and revocable fully hidden policy CP-ABE scheme for integrated demand response in multi-energy systems

Demand response is a crucial measure in multi-energy systems (MESs) that encourages energy service providers (ESPs) to engage with energy users (EUs). In the energy internet, EUs can match their energy source requirements through integrated demand response (IDR). However, such permissionless retrieval of energy data compromises the privacy of ESPs and exposes the energy consumption data of EUs. To address this, we propose a traceable, revocable fully hidden policy CP-ABE scheme called Poly-ABE for a data-driven IDR model in MESs, which allows registered EUs to request data and ESPs to authorize data decryption. Poly-ABE features fine-grained access policies using a matrix, coupled with hidden vector encryption to fully hide the policies. Matrix policies offer flexible and complex authorization rules with lower cost. Moreover, Poly-ABE constructs a decryption permission binary tree (DPBT) to identify the attribute permissions of EUs. If malicious behavior is detected, the DPBT is used to determine a minimum set of tree nodes to pinpoint malicious nodes. We rigorously demonstrate that Poly-ABE is selectively secure against selective access policy and chosen plaintext attacks. The experimental results substantiate the feasibility of Poly-ABE, highlighting its pre-authentication, traceability, revocation, and update capabilities.

Fast and Privacy-Preserving Attribute-Based Keyword Search in Cloud Document Services

Currently, various encryption techniques have been employed to protect the documents in cloud storage. In particular, attribute-based keyword search (ABKS) is a practical encryption primitive that can realize fine-grained access control and keyword based searching over encrypted documents. However, the search time in most of the existing ABKS schemes increases linearly with the size of document collection, which hinders the wide application of ABKS in cloud computing. To this end, we propose FAKS, a fast and privacy-preserving attribute-based keyword search system for cloud document services. Specifically, FAKS builds a Bloom filter tree structure from the document collection, which avoids matching keywords by traversing the entire collection. Then we introduce an attribute-based authenticated index retrieval (ABAIR) scheme to encrypt the Bloom filters in the tree node and retrieve the documents with the encrypted Bloom filters of the query keywords. Further, we give a concrete construction of FAKS from ABAIR to execute the keyword matching operations sublinearly in a top-down manner, and prove the security of FAKS against chosen keyword attack and keyword guessing attack. Finally, we conduct extensive experiments over the Wikipedia dataset, which show better and more stable search efficiency of FAKS compared to existing schemes.