Probabilistic Graph Research Articles

Inferring Advisor-Student Relationships from Publication Networks Based on Approximate MaxConfidence Measure

A publication network contains abundant knowledge about advisor-student relationships. However, these relationship labels are not explicitly shown and need to be identified based on the hidden knowledge. The exploration of such relationships can benefit many interesting applications such as expert finding and research community analysis and has already drawn many scholars’ attention. In this paper, based on the common knowledge that a student usually coauthors his papers with his advisor, we propose an approximateMaxConfidencemeasure and present an advisor-student relationship identification algorithm based on the proposed measure. Based on the comparison of two authors’ publication list, we first employ the proposed measure to determine the time interval that a potential advising relationship lasts and then infer the likelihood of this potential advising relationship. Our algorithm suggests an advisor for each student based on the inference results. The experiment results show that our algorithm can infer advisor-student relationships efficiently and achieve a better accuracy than the time-constrained probabilistic factor graph (TPFG) model without any supervised information. Also, we apply some reasonable restrictions on the dataset to reduce the search space significantly.

Recurrent neural network system using probability graph model optimization

We propose a novel recurrent neural network model based on a combination of the echo state network (ESN) and the dynamic Bayesian network (DBN). Our contribution includes the following: (1) A new graph-based echo state network (GESN) model is presented for nonlinear system modeling. The GESN consists of four layers: an input layer, reservoir layer, graph model optimization layer and output layer. Unlike the original ESN model, the GESN model uses probability graph inference to optimize reservoir states, and the optimized GESN is more robust. (2) A novel DBN graph is proposed for describing random probability signals in the GESN. Simultaneously, an inference algorithm of the DBN is deduced according to Bayesian rules and probability graph theory. Finally, GESN performance is tested using the Mackey---Glass time series and laser time series data forecasting The simulations and comparison results show that the proposed model is promising.

A GMM based uncertainty model for point clouds registration

The existing methods for the registration of point clouds acquired by laser scanners have some limitations. Firstly, as some samples of surface, a point cloud acquired by the laser scanner, which normally works in a spherical fashion, has very limited density when the surface is far away from the laser scanner and the density varies a lot at different ranges. Current registration methods cannot accurately model the surface uncertainty for such kind of point clouds of limited and large varying density. Secondly, when the point cloud is acquired while the platform is simultaneously moving, the estimation error of the platform motion makes the acquired point cloud distorted. To deal with these problems, in this paper, we propose an uncertainty model based on the Gaussian Mixture Model (GMM) to represent the point cloud. Specifically, we construct the GMM piece-wisely on the underlying surface of point cloud, which will accurately model the surface uncertainty. Also a hierarchical structure is employed to increase the robustness of the registration. Furthermore, by assigning each Gaussian component with a pose, a probabilistic graph can be constructed to tackle the problem of registration when the platform is moving while scanning. In this way the distorted point cloud, caused by the estimation error of the platform’s motion, can be corrected by performing graph optimization. Simulation and real world experimental results show that our method leads to better convergence than the state-of-the-art methods due to the accurate modeling of the surface uncertainty and the hierarchical structure, and it also enables us to correct the distorted point clouds.

New Probabilistic Multi-Graph Decomposition Model to Identify Consistent Human Brain Network Modules.

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding large-scale brain networks that underlie higher-level cognition in human. However, suitable network analysis computational tools are still lacking in human brain connectivity research. To address this problem, we propose a novel probabilistic multi-graph decomposition model to identify consistent network modules from the brain connectivity networks of the studied subjects. At first, we propose a new probabilistic graph decomposition model to address the high computational complexity issue in existing stochastic block models. After that, we further extend our new probabilistic graph decomposition model for multiple networks/graphs to identify the shared modules cross multiple brain networks by simultaneously incorporating multiple networks and predicting the hidden block state variables. We also derive an efficient optimization algorithm to solve the proposed objective and estimate the model parameters. We validate our method by analyzing both the weighted fiber connectivity networks constructed from DTI images and the standard human face image clustering benchmark data sets. The promising empirical results demonstrate the superior performance of our proposed method.

In-database batch and query-time inference over probabilistic graphical models using UDA–GIST

To meet customers' pressing demands, enterprise database vendors have been pushing advanced analytical techniques into databases. Most major DBMSes use user-defined aggregates (UDAs), a data-driven operator, to implement analytical techniques in parallel. However, UDAs alone are not sufficient to implement statistical algorithms where most of the work is performed by iterative transitions over a large state that cannot be naively partitioned due to data dependency. Typically, this type of statistical algorithm requires pre-processing to set up the large state in the first place and demands post-processing after the statistical inference. This paper presents general iterative state transition (GIST), a new database operator for parallel iterative state transitions over large states. GIST receives a state constructed by a UDA and then performs rounds of transitions on the state until it converges. A final UDA performs post-processing and result extraction. We argue that the combination of UDA and GIST (UDA---GIST) unifies data-parallel and state-parallel processing in a single system, thus significantly extending the analytical capabilities of DBMSes. We exemplify the framework through two high-profile batch applications: cross-document coreference, image denoising and one query-time inference application: marginal inference queries over probabilistic knowledge graphs. The 3 applications use probabilistic graphical models, which encode complex relationships of different variables and are powerful for a wide range of problems. We show that the in-database framework allows us to tackle a 27 times larger problem than a scalable distributed solution for the first application and achieves 43 times speedup over the state-of-the-art for the second application. For the third application, we implement query-time inference using the UDA---GIST framework and apply over a probabilistic knowledge graph, achieving 10 times speedup over sequential inference. To the best of our knowledge, this is the first in-database query-time inference engine over large probabilistic knowledge base. We show that the UDA---GIST framework for data- and graph-parallel computations can support both batch and query-time inference efficiently in databases.

Improving the process of driving a locomotive through the use of decision support systems

The process of driving a train was represented in the form of fuzzy situations, given in a table. The conformity between all possible situations and a set of driving decisions was established. The table size is determined by the number of situations which, in turn, depends on the degree of concretization of values. An algorithm of actions of a locomotive driver when driving a train is presented in the form of fuzzy probabilistic graph. Fuzzy numbers, the values of which are recorded in the matrix graph, represent the weights of transitions between vertices. The choice of decision by a locomotive decision support system (DSS) is carried out using the utility criterion. The training system is implemented with the use of the fuzzy classifier that represents fuzzy knowledge base, the input of which receives signals about current state of the traction rolling stock and of the environment. The model of dynamic knowledge base was obtained. As a result of analysis of existing types of intelligent systems, hierarchies, and algorithms of their work, taking into account the working conditions of locomotive crews and railway transport as a whole, the parameters for locomotive DSS were developed. We defined the minimal time it takes for a locomotive driver to make a decision about driving a train and to identify emergency situations. The functions of person that directly affect the efficiency and safety of the locomotive and require support using the intelligent systems were determined. The results of the work allow implementing intelligent DSS in modern locomotives. This will enhance the level of safety and efficiency of driving a train.

Medical Question Answering for Clinical Decision Support.

The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40%.

A Composite Network Approach for Assessing Multi-Species Connectivity: An Application to Road Defragmentation Prioritisation

One of the biggest challenges in large-scale conservation is quantifying connectivity at broad geographic scales and for a large set of species. Because connectivity analyses can be computationally intensive, and the planning process quite complex when multiple taxa are involved, assessing connectivity at large spatial extents for many species turns to be often intractable. Such limitation results in that conducted assessments are often partial by focusing on a few key species only, or are generic by considering a range of dispersal distances and a fixed set of areas to connect that are not directly linked to the actual spatial distribution or mobility of particular species. By using a graph theory framework, here we propose an approach to reduce computational effort and effectively consider large assemblages of species in obtaining multi-species connectivity priorities. We demonstrate the potential of the approach by identifying defragmentation priorities in the Italian road network focusing on medium and large terrestrial mammals. We show that by combining probabilistic species graphs prior to conducting the network analysis (i) it is possible to analyse connectivity once for all species simultaneously, obtaining conservation or restoration priorities that apply for the entire species assemblage; and that (ii) those priorities are well aligned with the ones that would be obtained by aggregating the results of separate connectivity analysis for each of the individual species. This approach offers great opportunities to extend connectivity assessments to large assemblages of species and broad geographic scales.

Probabilistic Factor Graph Based Approach for Automatic Material Assignments to Three-Dimensional Objects

There are strong correlations between material assignment, shape, and functionality of a part in an overall product/assembly. However, these strong correlations are rarely exploited for automated material assignment. We present a probabilistic graphical model to assign materials to the parts (components) of a 3D object (assembly) by identifying the relations between shape, functionality, and material of the parts. By learning the context-dependent correlation with supervision from a set of objects and their segmented parts, the learned model can be used to assign engineering materials to the parts of a query object. Our primary contributions are (a) the engineering materials definition and assignment and (b) assigning engineering materials based on the behavior and form of the parts in the object. The performance of the proposed computational approach is demonstrated by the results of material assignment on various query objects with prespecified engineering performance requirements.

Multilayer semantic segmentation of remote-sensing imagery using a hybrid object-based Markov random field model

ABSTRACTHigh spatial resolution (HR) remote-sensing image usually contains hierarchical semantic information. Many supervised methods have been developed to interpret this information through data training. In this article, without data training, a hybrid object-based Markov random field (HOMRF) model is proposed for multilayer semantic segmentation of remote-sensing images. In this method, label fields of different semantic layers are defined on the same region adjacency graph (RAG) of a given image, and a hybrid framework is suggested to capture and utilize the interactions within and between semantic layers by label fields. Namely a new transition probability matrix is introduced into the energy functions of label fields for describing the semantic context between layers, and the multilevel logistic model is employed to describe the interactions within the same layer. A principled probabilistic inference is developed to determine the optimal solution of the proposed method by iteratively updating each label field until convergence. The computational complexity of the proposed model is , where is the number of classes in all of the layers, is the number of sites in the probability graph of the MRF model, and is the number of iterations. Experimental results from various remote-sensing images demonstrate that the proposed method can produce higher segmentation accuracy than state-of-the-art MRF-based methods.

Learning production probabilities for musical grammars

While there is a growing body of work proposing grammars for music, there is little work testing analytical grammars in a generative setting. We explore the process of learning production probabilities for musical grammars from musical corpora and test the results using Kulitta, a recently developed framework for automated composition. To do this, we extend a well-known algorithm for learning production probabilities for context-free grammars (CFGs) to support various musical CFGs as well as an additional category of grammars called probabilistic temporal graph grammars.

Technique for Early Reliability Prediction of Software Components Using Behaviour Models.

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.

<inline-formula> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula>-Terminal Reliability of <inline-formula> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula>-Trapezoid Graphs

Given a probabilistic graph, with reliable edges and unreliable vertices, $K$ -terminal reliability problem is to find the probability that a given subset $K$ of vertices remains connected. The problem is #P-complete for general graphs, even so for some special graphs such as chordal graphs, and comparability graphs. However, polynomial time algorithms to solve the problem have been designed for some special graphs such as interval graphs, permutation graphs, and $d$ -trapezoid graphs. Existing time complexity of the polynomial time algorithm for the $d$ -trapezoid graph is $O(n^{2d+1})$ , which is a very high degree polynomial. This makes it impractical to solve large problem instances using the existing algorithm. Here, we propose a novel technique and use it to design a simple linear-time algorithm to solve the problem on $d$ -trapezoid graphs. As the complexity of our algorithm is linear, it is no more a difficulty to solve large problem instances in small amount of time.

Collaborative probabilistic labels for face recognition from single sample per person

Single sample per person (SSPP) recognition is one of the most challenging problems in face recognition (FR) due to the lack of information to predict the variations in the query sample. To address this problem, we propose in this paper a novel face recognition algorithm based on a robust collaborative representation (CR) and probabilistic graph model, which is called Collaborative Probabilistic Labels (CPL). First, by utilizing label propagation, we construct probabilistic labels for the samples in the generic training set corresponding to those in the gallery set, thus the discriminative information of the unlabeled data can be effectively explored in our method. Then, the adaptive variation type for a given test sample is automatically estimated. Finally, we propose a novel reconstruction-based classifier for the test sample with its corresponding adaptive dictionary and probabilistic labels. The proposed probabilistic graph based model is adaptively robust to various variations in face images, including illumination, expression, occlusion, pose, etc., and is able to reduce required training images to one sample per class. Experimental results on five widely used face databases are presented to demonstrate the efficacy of the proposed approach.

Image Segmentation via Probabilistic Graph Matching.

This paper presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as an inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low-level image cues and automatic tuning of parameters. The proposed scheme is experimentally shown to compare favorably with contemporary semi-supervised and unsupervised image segmentation schemes, when applied to contemporary state-of-the-art image sets.

Optimization of dialectical outcomes in dialogical argumentation

When informal arguments are presented, there may be imprecision in the language used, and so the audience may be uncertain as to the structure of the argument graph as intended by the presenter of the arguments. For a presenter of arguments, it is useful to know the audience's argument graph, but the presenter may be uncertain as to the structure of it. To model the uncertainty as to the structure of the argument graph in situations such as these, we can use probabilistic argument graphs. The set of subgraphs of an argument graph is a sample space. A probability value is assigned to each subgraph such that the sum is 1, thereby reflecting the uncertainty over which is the actual subgraph. We can then determine the probability that a particular set of arguments is included or excluded from an extension according to a particular Dung semantics. We represent and reason with extensions from a graph and from its subgraphs, using a logic of dialectical outcomes that we present. We harness this to define the notion of an argumentation lottery, which can be used by the audience to determine the expected utility of a debate, and can be used by the presenter to decide which arguments to present by choosing those that maximize expected utility. We investigate some of the options for using argumentation lotteries, and provide a computational evaluation.

Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming

Securing the networks of large organizations is technically challenging due to the complex configurations and constraints. Managing these networks requires rigorous and comprehensive analysis tools. A network administrator needs to identify vulnerable configurations, as well as tools for hardening the networks. Such networks usually have dynamic and fluidic structures, thus one may have incomplete information about the connectivity and availability of hosts. In this paper, we address the problem of statically performing a rigorous assessment of a set of network security defense strategies with the goal of reducing the probability of a successful large-scale attack in a dynamically changing and complex network architecture. We describe a probabilistic graph model and algorithms for analyzing the security of complex networks with the ultimate goal of reducing the probability of successful attacks. Our model naturally utilizes a scalable state-of-the-art optimization technique called sequential linear programming that is extensively applied and studied in various engineering problems. In comparison to related solutions on attack graphs, our probabilistic model provides mechanisms for expressing uncertainties in network configurations, which is not reported elsewhere. We have performed comprehensive experimental validation with real-world network configuration data of a sizable organization.

Fast image quality assessment via supervised iterative quantization method

No-reference/Blind image quality assessment (NR-IQA/BIQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train complex models such as support vector machine, neural network, and probability graph model, which result in long computational time and poor robustness. To overcome these weaknesses, the paper proposes a fast no-reference image quality assessment via hash coding method, named NRHC. First, the image is divided into overlapped patches to extract the spatial statistical features of natural scene images. Then the features are encoded to produce binary hash codes via supervised iterative quantization (SITQ) method. Finally, the Hamming distances between the hash code of the test image and those of original undistorted images are calculated to obtain the final image quality. Thorough experiments on benchmark databases demonstrate that the proposed approach achieves comparable performance and has higher computational efficiency and stronger robustness compared with the state-of-the-art NR-IQA methods.

A temperature spectrum density distribution based condition evaluation method and application in IGBT

Temperature measurement and calculation are frequently used in condition evaluation. In the paper, a temperature spectrum density method (TSDM) based on the statistic characteristics of thermal-image is proposed. Firstly, the temperature distribution of IGBT module for different condition was calculated, and based on the change of temperature distribution, the process for the TSDM is presented, including the building of temperature probability graph, calculation of kernel density, standard deviation (SD), temperature range (TR), and difference between peak and mean value. Examples analysis of IGBT were conducted, results indicate that the TSDM can track the condition change of IGBT module. Then, experimental investigation on the temperature spectrum density for different operation conditions were carried out, including the new and aged IGBT, and results demonstrate that temperature spectrum density changes with different conditions. Finally, influences of DC side voltage Vdc and switching frequency fsw were studied. It indicates that the temperature spectrum density parameters increase cubic polynomial with Vdc and linearly increase with switching frequency fsw.

Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases

Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources. In this paper, we formalize the RDF data by inconsistent probabilistic RDF graphs , which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus on an important problem, quality-aware subgraph matching over inconsistent probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high quality scores (considering both consistency and uncertainty). In order to efficiently answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning , which can greatly filter out false alarms of subgraphs. We also design an effective index to facilitate our proposed pruning methods, and propose an efficient approach for processing QA-gMatch queries. Finally, we demonstrate the efficiency and effectiveness of our proposed approaches through extensive experiments.