Tree-structured Graph Research Articles

Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection

Rumor detection on social media has become increasingly important. Most existing graph-based models presume rumor propagation trees (RPTs) have deep structures and learn sequential stance features along branches. However, through statistical analysis on real-world datasets, we find RPTs exhibit wide structures, with most nodes being shallow 1-level replies. To focus learning on intensive substructures, we propose Rumor Adaptive Graph Contrastive Learning (RAGCL) method with adaptive view augmentation guided by node centralities. We summarize three principles for RPT augmentation: 1) exempt root nodes, 2) retain deep reply nodes, 3) preserve lower-level nodes in deep sections. We employ node dropping, attribute masking and edge dropping with probabilities from centrality-based importance scores to generate views. A graph contrastive objective then learns robust rumor representations. Extensive experiments on four benchmark datasets demonstrate RAGCL outperforms state-of-the-art methods. Our work reveals the wide-structure nature of RPTs and contributes an effective graph contrastive learning approach tailored for rumor detection through principled adaptive augmentation. The proposed principles and augmentation techniques can potentially benefit other applications involving tree-structured graphs.

Virtual machine tree task scheduling for load balancing in cloud computing

The increasing number of publications towards cloud computing proves that much research and development has been done, especially for task scheduling. Organizations are eager to get more customized technology to run the most smoothly in the provision of visual cloud services for fruity users. As the circumstances of Covid indicate to technology that everyone should run digitally, the workload on machines increased. For workload solutions, organizations are trying to balance the situation with the successful operation of cloud services to use appropriate services/resources. Nevertheless, the issues are still to be resolved by researchers, so we respect all my friends who are putting a lot of effort into developing new techniques. A proposed paper is showing a new collation with the load balancing factor by implementing quality of service (QoS) and virtual machine tree (VMT). A CloudSim toolkit will then be used to compare them. A tree structure graph is included in the VMT algorithm to schedule tasks with the appropriate distribution on each machine. The QoS algorithm performs the task of scheduling based on the service required by the user with the best quality and satisfies the user.

A modified salp swarm algorithm for task assignment problem

The task assignment problem (TAP) is one of the standard combinatorial optimization problems (COPs) in the field of discrete optimization. TAP is known to be an NP-complete problem due to the difficulty to obtain the exact solution of it in polynomial time. Thus, it is essential to develop and/or propose an optimization algorithm to solve this problem. In TAPs, a set of tasks is assigned to a set of machines to both effectively minimize the communication and execution cost. This paper presents a modified Salp Swarm Algorithm (SSA), to solve not only task assignment problems but also, fundamental combinatorial optimization problems in engineering and real-world scientific domains. The modified salp algorithm takes place in the integration with the Local Refinement Heuristic (LRH) approach to enhance a given assignment along with operators. The modified algorithm is named Modified Salp Local Refinement Heuristic (MSLRH). To the best of our knowledge, this paper is the first of its kind to attempt using the SSA in task assignment problems. The MSLRH algorithm is tested on different benchmark datasets (tree structure and general graph), including various tasks and machines for each dataset. In addition, it compared with the most known meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Jaya algorithm (JAYA) to investigate the effectiveness of the MSLRH algorithm in terms of average assignment allocation cost. From tree structure dataset (e.g., 200 tasks assigned to 8 machines), the proposed MSLRH algorithm has achieved a minimum average assignment cost better than GA by 62% and better than PSO and JAYA by 42%. From the general graph dataset (e.g., 209 tasks assigned to 16 machines), the proposed MSLRH algorithm has better results than other algorithms up to 60%. From various and extensive experimental results, the proposed algorithm has proven the effectiveness in solving the task assignment problem.

A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images

This paper investigates the problem of human pose estimation (HPE) from single 2-dimensional (2D) still images using a convolutional neural network (CNN). The aim was to train the CNN to analyze a 2D input image of a person to determine the person's pose. The CNN output was given in the form of a tree-structured graph of interconnected nodes representing 2D image coordinates of the person's body joints. A new data-driven tree-based model for HPE was validated and compared to the traditional anatomy-based tree-based structures. The effect of the number of nodes in anatomy-based tree-based structures on the accuracy of HPE was examined. The tree-based techniques were compared with non-tree-based methods using a common HPE framework and a benchmark dataset. As a result of this investigation, a new hybrid two-stage approach to the HPE estimation was proposed. In the first stage, a non-tree-based network was used to generate approximate results that were then passed for further refinement to the second, tree-based stage. Experimental results showed that both of the proposed methods, the data-driven tree-based model (TD_26) and the hybrid model (H_26_2B) lead to very similar results, obtaining 1% higher HPE accuracy compared to the benchmark anatomy-based model (TA_26) and 3% higher accuracy compared to the non-tree-based benchmark (NT_26_A). The best overall HPE results were obtained using the anatomy-based benchmark with the number of nodes increased from 26 to 50, which also significantly increased the computational cost.

Governing convergence of Max-sum on DCOPs through damping and splitting

Max-sum is a version of Belief Propagation, used for solving DCOPs. In tree-structured problems, Max-sum converges to the optimal solution in linear time. Unfortunately, when the constraint graph representing the problem includes multiple cycles (as in many standard DCOP benchmarks), Max-sum does not converge and explores low quality solutions. Recent attempts to address this limitation proposed versions of Max-sum that guarantee convergence, while ignoring some of the problem's constraints. Damping is a method that is often used for increasing the chances that Belief Propagation will converge. That being said, it has not been suggested for inclusion in the algorithms that propose Max-sum for solving DCOPs.In this paper we advance the research on incomplete-inference DCOP algorithms by: 1) investigating the effect of damping on Max-sum. We prove that, while damping slows down the propagation of information among agents, on tree-structured graphs, Max-sum with damping is guaranteed to converge to the optimal solution in weakly polynomial time; and 2) proposing a novel method for adjusting the level of asymmetry in the factor graph, in order to achieve a balance between exploitation and exploration, when using Max-sum for solving DCOPs. By converting a standard factor graph to an equivalent split constraint factor graph (SCFG), in which each function-node is split into two function-nodes, we can control the level of asymmetry for each constraint.Our empirical results demonstrate a drastic improvement in the performance of Max-sum when using damping (referred to herein as Damped Max-sum, DMS). However, in contrast to the common assumption that Max-sum performs best when converging, we demonstrate that non converging versions perform efficient exploration, and produce high quality results, when implemented within an anytime framework. On most standard benchmarks, the best results were achieved using versions with a high damping factor, which outperformed existing incomplete DCOP algorithms. In addition, our results imply that by applying DMS to SCFGs with a minor level of asymmetry, we can find high quality solutions within a small number of iterations, even without using an anytime framework. We prove that for a factor graph with a single constraint, if this constraint is split symmetrically, Max-sum applied to the resulting cycle is guaranteed to converge to the optimal solution. We further demonstrate that for an asymmetric split, convergence is not guaranteed.

Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database.

Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our paper provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin, and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model.

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

Machine learning in pain research.

Machine learning in pain research.

Multi-criteria optimization strategies for tree-structured production chains

The aim of this paper is presentation of some optimization strategies applicable in the optimization of multi-stage and multi-thread chain structures (linear and tree-structured acyclic graphs) with multiple intermediate goal functions. The inspirations for this type of analysis are production chains often seen in industrial plants. Production in these plants consists of sequences of multiple units connected linearly or in tree-structured graphs in which, at various production stages, intermediate quality criteria, related to the semi-products, may have to be considered. Optimization strategies as well as the results of their application for different hypothetical problems and the real metallurgical problem of lead production are presented.

Hardware/Software Partitioning for Heterogenous MPSoC Considering Communication Overhead

Hardware/software partitioning (HSP) is an important step in the co-design of hardware/software. This paper addresses the problem of HSP with communication (HSPC) on heterogeneous multiprocessor system-on-chip (MPSoC). The classic HSP is modeled as an optimization problem with an objective of minimizing the finishing time in system under the hardware area constraints. First, we put forward an optimal method, integer linear programming (ILP) algorithm, for solving the problem for small inputs. Then, we propose two other algorithms using dynamic programming (DP) method, i.e., Optimal Tree Partitioning (OTP) method for tree-structured graphs and Tree Cover Partitioning (TCP) algorithm for general graphs in polynomial time. The overall performance of the proposed algorithms is evaluated through comparisons with that of a genetic algorithm (GA) and a greedy algorithm which are commonly used to solve HSP problem. We have conducted experimental performance evaluation on various benchmarks with different combinations of computation to communication ratios and hardware area constraints. The experimental results show that OTP algorithm can generate optimal solutions with much faster speed than ILP, and TCP algorithm can obtain near-optimum with higher quality than those produced by GA and greedy algorithm.

Automatic Ear Landmark Localization, Segmentation, and Pose Classification in Range Images

Multibiometric systems using face and ear features are increasingly adopted for forensic and civilian applications to address the challenges of facial expressions and occlusions. Although numerous ear and face recognition techniques have been proposed, not much work has been conducted in the field of 3-D fiducial points localization and 3-D ear detection. This paper presents an effective and efficient system of ear landmark localization, ear detection, and pose classification based on 3-D ears captured under large yaw variations. By utilizing the symmetrical property of human heads and classifying the ear with respect to its pose, all three tasks can be fulfilled given either left or right ears, without any prior pose information. A novel ear tree-structured graph (ETG) is proposed to represent the 3-D ear, after which a 3-D flexible mixture model is trained to locate the landmarks automatically. Then, the ear region is segmented based on them and the pose of the ear, i.e., whether it is a left or right ear, is classified based on the detected ETG. To the best of our knowledge, this paper is the first to present automatic landmark localization of 3-D ears extracted from facial scans with significant pose variations. Experiments were conducted at the University of Notre Dame collection F, G and J2, which contain large occlusion and pose variations, validating the effectiveness of the proposed methods.

3D Ear Segmentation and Classification Through Indexing

Current growth trends in different biometrics applications present challenges to researchers. To address these challenges, we need new data storage and retrieval techniques to make the recognition process time efficient. This paper presents a system for time efficient 3D ear biometrics from a large biometrics database. The proposed system has two components that are primarily responsible for: 1) automatic 3D ear segmentation and 2) hierarchical categorization of the 3D ear database using the shape information and surface depth information, respectively. We use an active contour algorithm along with a tree-structured graph to segment the ear region from the 3D profile images. The segmented 3D ear database is then categorized based on the geometrical feature values, computed from the ear shape, into oval, round, rectangular, and triangular categories. For the categorization based on the depth information, the feature space is partitioned using tree-based indexing techniques. We used indexing techniques with balanced split (k-dimensional (KD) tree) and unbalanced split (pyramid tree) data structures to categorize the database separately and then compared their retrieval efficiency. Experiments are conducted to compare the average computation time per query when performing recognition through hierarchical categorization with the average computation time when recognition is based on sequential search. Experimental results without indexing conducted on the University of Notre Dame Collection J2 data set yielded a rank-one recognition rate of 98.5%. We applied the indexing technique to compute the rank-one recognition accuracy with a 10%–50% search space reduction using a 10% step size. With 10%, 20%, 30%, 40%, and 50% search space reduction the rank-one recognition accuracy gracefully degrades to 96.87%, 96.14%, 95.18%, 94.21%, and 93.49%, respectively, while performing nearly 3, 3.3, 4, 4.2, and 5 times faster than the state-of-the-art technique that uses sequential search.

Multiscale quadtree model fusion with super-resolution for blocky artefact removal

Digital elevation model (DEM) integration by optimally fusing multiple measurements having different characteristics can play an important role in estimation and prediction of environmental changes and natural disasters. Multiscale or multiresolution modelling used for this purpose has attracted attention in terms of its rich modelling capability as well as computational efficiency. In particular, a multiscale Kalman smoother (MKS) with tree structure graph can provide a powerful, efficient algorithm based on the Markov property. However, due to the stair-like correlation from the tree structure, unrealistic artefacts can be generated in the fusion result. This is especially prominent on geographic surfaces or ocean surfaces that have naturally smooth characteristics. In this article, a super-resolution (SR) algorithm is applied to remove blocky artefacts. Using the algorithm, new measurements are generated in the area where blocky artefacts can arise. The measurement is then fused with vector-valued MKS. The results show that this novel image fusion scheme provides a smaller root mean square error (RMSE) as well as better visual inspection.

Stochastic Belief Propagation: A Low-Complexity Alternative to the Sum-Product Algorithm

The belief propagation (BP) or sum-product algorithm is a widely used message-passing method for computing marginal distributions in graphical models. At the core of the BP message updates, when applied to a graphical model involving discrete variables with pairwise interactions, lies a matrix-vector product with complexity that is quadratic in the state dimension <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , and requires transmission of a ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -1)-dimensional vector of real numbers (messages) to its neighbors. Since various applications involve very large state dimensions, such computation and communication complexities can be prohibitively complex. In this paper, we propose a low-complexity variant of BP, referred to as stochastic belief propagation (SBP). As suggested by the name, it is an adaptively randomized version of the BP message updates in which each node passes randomly chosen information to each of its neighbors. The SBP message updates reduce the computational complexity (per iteration) from quadratic to linear in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , without assuming any particular structure of the potentials, and also reduce the communication complexity significantly, requiring only log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> bits transmission per edge. Moreover, we establish a number of theoretical guarantees for the performance of SBP, showing that it converges almost surely to the BP fixed point for any tree-structured graph, and for any graph with cycles satisfying a contractivity condition. In addition, for these graphical models, we provide nonasymptotic upper bounds on the convergence rate, showing that the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm of the error vector decays no slower than <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/√ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ) with the number of iterations <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> on trees and the normalized mean-squared error decays as <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ) for general graphs. This analysis, also supported by experimental results, shows that SBP can provably yield reductions in computational and communication complexities for various classes of graphical models.

Decision support in e-business based on assessing similarities between ontologies

As ontology usage becomes more prevalent in e-business decision support systems, it is essential to establish interoperation between agents or web services with different ontologies. Ontology matching represents a promising technique in information integration, agent communication and web services. Here, an ontology similarity assessment algorithm is proposed to map concepts and properties between ontologies of semantic web services. It has the two phases (1) syntactic analysis based on string tokenisation, and (2) semantic analysis employing the WordNet thesaurus and tree-structured graphs with the Tversky model. Promising experimental results indicate that this algorithm can find matching results with relatively high precision.

Self-Validated Labeling of Markov Random Fields for Image Segmentation

This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph cuts (GGC), a new technique that extends the binary s-t graph cut for self-validated labeling. Specifically, we use the split-and-merge strategy to decompose the complex problem to a series of tractable subproblems. In terms of Gibbs energy minimization, a suboptimal labeling is gradually obtained based upon a set of cluster-level operations. By using different optimization structures, we propose three practical algorithms: tree-structured graph cuts (TSGC), net-structured graph cuts (NSGC), and hierarchical graph cuts (HGC). In contrast to previous methods, the proposed algorithms can automatically determine the number of labels, properly balance the labeling accuracy, spatial coherence, and the labeling cost (i.e., the number of labels), and are computationally efficient, independent to initialization, and able to converge to good local minima of the objective energy function. We apply the proposed algorithms to natural image segmentation. Experimental results show that our algorithms produce generally feasible segmentations for benchmark data sets, and outperform alternative methods in terms of robustness to noise, speed, and preservation of soft boundaries.

Iterative approach to operation selection and sequencing in process planning

This paper considers the problem of selecting and sequencing operations in process planning for the objective of minimizing the sum of operation processing costs and machine, set-up and tool change costs. The main constraint is the precedence relations among operations. To represent the precedence relations and alternative operations, a tree-structured precedence graph is suggested. Based on the graph, the entire problem is decomposed into two subproblems: operation selection and operation sequencing. Then, three iterative algorithms are suggested that solve the two subproblems iteratively until optimal and near-optimal solutions are obtained. The algorithms are illustrated using an example part, and to show the performances of the algorithms, computational experiments were done on randomly generated test problems. The results show that the algorithms suggested work well for the test problems.

Comments on "Minimum number of adders for implementing a multiplier and its application to the design of multiplierless digital filters"

For the original paper see ibid., vol. 42 p. 453-460 (July 1995). In the aforementioned paper by D. Li, a method of designing integer multipliers that uses fewer adders than canonic signed-digit (CSD) coding was described. This method is claimed by the author to be optimal, i.e., it produces a multiplier with the least possible adders. The commenters claim that they have presented a similar method, implemented using the MAG algorithm, to perform the same task, which was shown to be optimal. Comparing the two techniques shows that Li's method is not necessarily optimal. Both methods perform an exhaustive search over a set of multiplier configurations. In the commenters' paper, they described these configurations in terms of graphs. It is said that this method proves to be quite useful in that it becomes easy to describe the cases not covered by Li's algorithm. It is stated that the algorithm proposed by Li is suboptimal because: 1) graphs produced using fundamentals (intermediate vertices) of lower cost graphs are not considered; 2) tree-structured graphs are not considered; 3) right shifts (divide-by-two) are not allowed, causing some multipliers to require too many adders.

Computer-aided design using the method of cut-joint kinematic constraints

A method for automatically altering the geometry of mechanical parts due to a change in design parameters is presented. The formulation for computing the effects of changes in design parameters of mechanical parts and mechanisms is based upon kinematic criteria adopted from mathematical formulations used in multibody dynamics analyses. The modelling method presented is based upon the representation of the geometry of a mechanical part using a combination of links and joints. Constraints describing the relation between two links are specified. A cut-joint method is used to transform mechanisms with closed loops into a tree structure graph. Variational displacements and orientation matrices are then derived with respect to design parameters. The formulation is used to compute new geometric design parameters after an alteration in the initial design. The formulation is demonstrated using a numerical example.

Path signatures: a way to speed up recursion in relational databases

Composite objects often involve recursive relationships, so-called bills-of-materials, which are cumbersome to handle in relational database systems. The relationships constitute a directed graph, where the successors of a node represent its components, recursively. Instead of the whole transitive closure (all ancestor-descendant pairs), the task is to retrieve the descendants of any given node. A simple relational solution is suggested, which packs information of the ancestor path of each node into a fixed-length code, called the signature. The code is nonunique, and its purpose is to define a relatively small superset of the descendants, as well as to establish a basis for clustering. It supports effective retrieval of the descendants, in terms of both disk accesses and DBMS calls. The method performs best for tree-structured graphs, where the processing time typically decreases by a factor of more than 10, compared to a simple loop of joins. Also, general directed graphs, both acyclic and cyclic, can be processed more effectively. The method is implemented on top of a relational system, but advantages can be gained on other platforms, too.