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Constructing Service Semantic Link Network Based on the probabilistic graphical model

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2012年第6期5卷 1040-1051页

作者： Zhao, Anping Ma, Yan Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

Automatic services collaboration calls for the development of semantically structured service network to maximize the utility of Web services. Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by the dependency relationship between services. We provided an effective method for constructing S-SLN based on the graphical structure representation of the dependencies embedded in a probabilistic model. A Markov network is an undirected graph whose links represents probability dependencies. We first learned Markov network structure from Web services data, and then transformed the undirected Markov network structure into a directed graph structure of S-SLN based on the same joint probability distribution. Finally, experimental results show the effectiveness of the method.

关键词： Web service probabilistic graphical model S-SLN Service relationship

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Inference of Cancer Progression With probabilistic graphical model From Cross-Sectional Mutation Data

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IEEE ACCESS 2018年 6卷 22889-22898页

作者： Zhang, Wei Wang, Shu-Lin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

With the advance of high-throughput sequencing technologies, a great amount of somatic mutation data in cancer have been produced, allowing deep analyzing tumor pathogenesis. However, the majority of these data are cross-sectional rather than temporal, and it is difficulty to infer the temporal order of gene mutations from them. In this paper, we first show a probabilistic graphical model (PGM) to infer the temporal order constrains and selectivity relation among the mutation of cancer driver genes which are presented by a directed acyclic graph. We then apply an exponential function based on the mutation probability of these driver genes to obtain their mutation waiting time which can be used to induce mutually exclusive driver pathways. Finally, we evaluate the performance of the PGM both on simulated data and real-cancer somatic mutation data. The experimental results and comparative analysis reveal that the PGM can capture most of the selectivity relation of mutated driver genes which have been validated by previous works. Furthermore, the PGM can provide new insights on simultaneously inferring driver pathways and the temporal order of their mutations from cross-sectional data.

关键词： probabilistic graphical model somatic mutation cancer progression driver pathway waiting time

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Action network: a probabilistic graphical model for social simulation

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR modelING AND SIMULATION INTERNATIONAL 2022年第4期98卷 335-346页

作者： Zakaria, Nordin Univ Teknol PETRONAS High Performance Cloud Comp Ctr Tronoh 31750 Malaysia

Agent-based social simulations are typically described in imperative form. While this facilitates implementation as computer programs, it makes implicit the different assumptions made, both about the functional form and the causal ordering involved. As a solution to the problem, a probabilistic graphical model, Action Network (AN), is proposed in this paper for social simulation. Simulation variables are represented by nodes, and causal links by edges. An Action Table is associated with each node, describing incremental probabilistic actions to be performed in response to fuzzy parental states. AN offers a graphical causal model that captures the dynamics of a social process. Details of the formalism are presented along with illustrative examples. Software that implements the formalism is available at http://***

关键词： Agent-based modeling probabilistic graphical model simulation language

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Feature Matching via Motion-Consistency Driven probabilistic graphical model

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2022年第9期130卷 2249-2264页

作者： Ma, Jiayi Fan, Aoxiang Jiang, Xingyu Xiao, Guobao Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Peoples R China

This paper proposes an effective method, termed as motion-consistency driven matching (MCDM), for mismatch removal from given tentative correspondences between two feature sets. In particular, we regard each correspondence as a hypothetical node, and formulate the matching problem into a probabilistic graphical model to infer the state of each node (e.g., true or false correspondence). By investigating the motion consistency of true correspondences, a general prior is incorporated into our formulation to differentiate false correspondences from the true ones. The final inference is casted into an integer quadratic programming problem, and the solution is obtained by using an efficient optimization technique based on the Frank-Wolfe algorithm. Extensive experiments on general feature matching, as well as fundamental matrix estimation, relative pose estimation and loop-closure detection, demonstrate that our MCDM possesses strong generalization ability as well as high accuracy, which outperforms state-of-the-art methods. Meanwhile, due to the low computational complexity, the proposed method is efficient for practical feature matching tasks.

关键词： Feature matching probabilistic graphical model Motion-consistency Robust estimation Outlier

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Fully automated segmentation of pneumonia infection based on probabilistic graphical model and U-Net blend network

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2022年第6期10卷 608-615页

作者： Xia, Xunpeng Zhang, Rongfu Yao, Xufeng Huang, Gang Tang, Tiequn Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Shanghai Univ Med & Hlth Sci Coll Med Imaging Shanghai Peoples R China

COVID-19 spread rapidly in the global world, causing a serious medical treatment crisis. Automated segmentation of pulmonary infection from Computed Tomography (CT) images strengthened the traditional treatment strategy against COVID-19. We proposed an automated fully lung CT image infection segmentation framework named probabilistic graphical model U-Net (PGM-U-Net). The whole framework iterative training end-to-end with general back-propagation mechanism with minor computational overhead from PGM component. The U-Net feature extractor benefits from modelling spatial correlations through the PGM component. Compared to the baseline network without modelling spatial correlations, experimental results illustrate that the proposed PGM-U-Net framework achieves higher accuracy probability maps of region predictions in the isolated infection regions. For further quantitative comparison experiment, we demonstrate that our framework outperforms the existing methods in pulmonary infection segmentation and achieves the Free-response Receiver Operating Characteristic Curve (FROC) score of 0.912 on the test data set.

关键词： Pulmonary infection COVID-19 segmentation of lesions probabilistic graphical model

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A probabilistic graphical model for Social IoT-based Indoor Air Quality Monitoring in Smart Villages 20

A Probabilistic Graphical Model for Social IoT-based Indoor ...

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2024 International Conference on Wireless and Mobile Computing Networking and Communications

作者： Ahmadinabi, Sajad Soorki, Mehdi Naderi Aghajari, Hossein Jafari, Amir Reza Ranjbaran, Sara Shahid Chamran Univ Ahvaz Ahvaz Iran Samovar Telecom SudParis Paris France Aalto Univ Espoo Finland

ISBN: (纸本)9798350387452;9798350387445

The smart village is a promising approach for achieving socio-economic sustainability in rural areas. This paper utilizes Social Internet of Things (SIoT) methodologies to realize the smart village concept through efficient and cost-effective IoT technology. Each physical sensor and IoT device has a virtual counterpart Digital Twin (DT) at the edge for effective data analysis and optimization. For emerging public health services, monitoring indoor air quality (IAQ) in critical rural buildings is crucial. This paper proposes a probabilistic graphical model to capture IAQ changes using low-cost LoRa end nodes (EN) and gateway devices. These devices measure light intensity, temperature, and polluting gas concentration levels. The unsupervised k-means algorithm clusters the real-time IAQ data. At the same time, a Markov-based model visualizes and predicts IAQ changes. The model parameters are updated in real-time using data from a deployed LoRa wireless network. The framework was evaluated in rural areas near Ghaletol, Khuzestan province, Iran, with deployments in schools, agricultural warehouses, medical centers, and supermarkets. The best IAQ Markov states were 3 for schools, 3 for agricultural warehouses, 4 for medical centers, and 5 for supermarkets. For instance, the supermarket's IAQ model showed a polluting gas concentration of 862.6 ppm, an indoor temperature of 28.66 degrees C, and a light intensity of 70.05 Lux.

关键词： LoRa technology Social IoT (SIoT) probabilistic graphical model Smart Village Indoor Air Quality Monitoring

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A probabilistic graphical model Approach for Human Activity Recognition using Skeleton Data 2

A Probabilistic Graphical Model Approach for Human Activity ...

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2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Bayat, Amir Hossein Arzani, Mohammad Mahdi Fathy, Mahmood Matinnejad, Ali Minaei-Bidgoli, Behrouz Entezari, Rahim Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781509058204

In this paper we introduce a general probabilistic graphical model for human everyday activity recognition. The proposed model is a discriminative graphical model with hidden variables for modeling body pose and sequential order of them. We use a unified framework for prediction task that is faster and more efficient than structured support vector machine and hidden conditional random fields. We have trained and tested the model on RGB-D videos and the result was comparable to the state of the art.

关键词： activity recognition probabilistic graphical model discriminative models skeleton

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Analysis of composite gestures with a coherent probabilistic graphical model

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Virtual Reality 2005年第4期8卷 242-252页

作者： Corso, Jason J. Ye, Guangqi Hager, Gregory D. Computational Interaction and Robotics Lab. The Johns Hopkins University Baltimore MD 21218 United States

Traditionally, gesture-based interaction in virtual environments is composed of either static, posture-based gesture primitives or temporally analyzed dynamic primitives. However, it would be ideal to incorporate both static and dynamic gestures to fully utilize the potential of gesture-based interaction. To that end, we propose a probabilistic framework that incorporates both static and dynamic gesture primitives. We call these primitives Gesture Words (GWords). Using a probabilistic graphical model (PGM), we integrate these heterogeneous GWords and a high-level language model in a coherent fashion. Composite gestures are represented as stochastic paths through the PGM. A gesture is analyzed by finding the path that maximizes the likelihood on the PGM with respect to the video sequence. To facilitate online computation, we propose a greedy algorithm for performing inference on the PGM. The parameters of the PGM can be learned via three different methods: supervised, unsupervised, and hybrid. We have implemented the PGM model for a gesture set of ten GWords with six composite gestures. The experimental results show that the PGM can accurately recognize composite gestures. © Springer-Verlag London Limited 2005.

关键词： Gesture recognition Hand postures Human computer interaction probabilistic graphical model Vision-based interaction

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Detection of Sea Surface Obstacle Based on Super-pixel probabilistic graphical model and Sea-sky-line 11

Detection of Sea Surface Obstacle Based on Super-pixel Proba...

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11th International Conference on Machine Vision (ICMV)

作者： Zhu, Liting. Liu, Jingyi. Chen, Jinbo. Shanghai Univ Dept Precis Mech Engn Shanghai 200444 Peoples R China

ISBN: (数字)9781510627499

ISBN: (纸本)9781510627499

With the development of marine resources, the USV (Unmanned Surface Vehicle) was widely used as a platform for autonomous navigation in the marine environment. In order to ensure the safe navigation of USV, this paper proposed a sea surface obstacle detection method based on probability graphical model and sea-sky-line. Our method utilized the SLIC algorithm to segment the sea surface image for image pre-processing. Then, we proposed the superpixel-based probability graphical model to segment the image, and the sea surface image would be divided into three main semantic regions and an obstacle region. Finally, we proposed a sea-sky-line detection algorithm. Based on this, obstacles within the sea-sky-line would be detected. The accuracy of this method has reached 82.1%, and the recall rate has reached 92.0%. The method can effectively avoid the interference of sea surface reflection and objects such as clouds in the sky, and has a good effect on the detection of obstacles.

关键词： probabilistic graphical model super-pixel segmentation sea-sky-line detection obstacle detection

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A probabilistic graphical model for Predicting Cascade Failures of Electric Vehicle Charging Networks Caused by Hurricanes

A Probabilistic Graphical Model for Predicting Cascade Failu...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Zhang, Tianze Fan, Pengyu Tang, Difei Nanjing Normal Univ NARI Sch Elect & Automat Engn Nanjing Peoples R China

ISBN: (纸本)9781665464413

Rapid proliferation of electric vehicles (EVs) accelerates the construction of charging stations, evolving into EV charging networks (EVCNs). Extreme weather may trigger the cascading failures in the EVCNs, affecting the stable operation of EV charging stations. This paper proposes an approach for predicting the cascading failures caused by hurricane considering the load shedding and the following EV load redistribution. A Bayesian network based probabilistic graphical model is established to represent the interaction between the outages of charging stations. An AC-based Cascading Failure model (ACCF) is utilized to identify the overload branch and conduct load shedding. The load redistribution is modeled by the effective resistance between the nearby charging stations. In the case study, we predict the probability of failure of EV charging systems to verify the effectiveness of the method by comparing real data for different hurricane scenarios. The result indicates that the load redistribution should not be ignored when predicting the cascading failures of EVCNs.

关键词： Electric vehicle Charging network probabilistic graphical model Bayesian Network

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