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Checking Beliefs in Dynamic Networks

Nuno P. Lopes, Nikolaj Bjørner, Patrice Godefroid, Karthick Jayaraman, George Varghese
2015 Symposium on Networked Systems Design and Implementation  
to the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI '15) is sponsored by USENIX  ...  As a consequence, NoD allows checking for beliefs about network reachability policies in dynamic networks.  ...  The belief templates in Table 1 abstract a vast majority of specific checks in our network and probably other networks. A GUI interface for belief templates will help operators.  ... 
dblp:conf/nsdi/LopesBGJV15 fatcat:pdm3p5r54bbi3gmq3mke3tztdm

Automatic belief network modeling via policy inference for SDN fault localization

Yongning Tang, Guang Cheng, Zhiwei Xu, Feng Chen, Khalid Elmansor, Yangxuan Wu
2016 Journal of Internet Services and Applications  
The evaluation shows that MPI is a highly scalable, effective and flexible modeling approach to tackle fault localization challenges in a highly dynamic and agile SDN network.  ...  In this paper, we propose a new approach to tackle SDN fault localization by automatically Modeling via Policy Inference (called MPI) the causality between SDN faults and their symptoms to a belief network  ...  Then we dynamically changing the number of policies from 10 up to 10,000 to check (1) the required system memory for storing and constructing the belief network based on the policy input; and (2) the agility  ... 
doi:10.1186/s13174-016-0043-y fatcat:ny7oyp66vjdlrehwhs4hdpi7he

Optimal Progressive Error Recovery for Wireless Sensor Networks using Irregular LDPC Codes

Saad B. Qaisar, Hayder Radha
2007 2007 41st Annual Conference on Information Sciences and Systems  
Precisely, we present a novel framework for processing within the network (in-network) using irregular low density parity check (LDPC) codes for channel coding, in which nodes progressively decode the  ...  We use density evolution algorithm for belief propagation decoding of LDPC codes to cast the optimization problem and use dynamic programming to reach the solution.  ...  for in-network error recovery as:IV.  ... 
doi:10.1109/ciss.2007.4298305 dblp:conf/ciss/QaisarR07 fatcat:d6m7y5ebvjgc7dmwfwj3oo6jsm

A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects [chapter]

Mohamad Mostafa, Giuseppe Oliveri, Werner G. Teich, Jürgen Lindner
2016 Artificial Neural Networks - Models and Applications  
In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI).  ...  We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework.  ...  Dynamical behavior of belief propagation In a series of papers, Hemati et. al.  ... 
doi:10.5772/63387 fatcat:hp6zcfriuzb3jb3vgcysbkc4nm

Business Process Event Log Transformation into Bayesian Belief Network

Titas Savickas, Olegas Vasilecas
2014 Information Systems Development  
Bayesian belief networks are a probabilistic modelling tool and the paper presents their application in BP mining.  ...  The paper presents results of a synthetic log transformation into Bayesian belief network showing possible application in business intelligence extraction and improved decision support capabilities.  ...  Bayesian belief network in factor graph form Table 1 . 1 Bayesian belief network inference results Question  ... 
dblp:conf/isdevel/SavickasV14 fatcat:mj4sxckaqzahbaaryzwu3dclyq

Constraint Relaxation and Annealed Belief Propagation for Binary Networks

Yen-Chih Chen, Yu T. Su
2007 2007 IEEE International Symposium on Information Theory  
In this paper, we propose two novel generalized belief propagation (BP) algorithms to improve the convergence behavior of the conventional BP algorithm.  ...  For decoding turbo-like error correcting codes, we adopt a parametric Gaussian approximation to relax the binary parity check constraints and generalize the conventional binary networks as well.  ...  In section II, we formulate the free energy of a belief network by taking into consideration the temperature effect and give the corresponding BP equations.  ... 
doi:10.1109/isit.2007.4557246 dblp:conf/isit/ChenS07 fatcat:jsokuanfp5hbxk7yanvve6ganu

Forthcoming Papers

2003 Artificial Intelligence  
The proposed approach to stable model checking has been implemented in DLV-a state-of-theart implementation of DLP.  ...  The transformation is parsimonious (i.e., no new symbol is added), and efficiently computable, as it runs in logarithmic space (and therefore in polynomial time).  ...  , in particular, includes both polytrees and two-level networks.  ... 
doi:10.1016/s0004-3702(03)00180-2 fatcat:4wdss2ra6ja6poxv4t7uvtl3pq

An Enhanced Approach for a Multiagent Distributed Context Using Tree Topology and Transferable Belief Model

Neelam Rajpoot, Abhishek Mathur, Shailendra Shrivastava
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
The major utilization of transferable belief model is in sensor networks.  ...  Dynamic scenario is been considered where agents gather data that changes over time. A cyclic graph algorithm has been proposed to merge to basic belief assignment based on transferable belief model.  ...  Then it will check this till the destination node. In figure 3 data range is 1.5 meters.  ... 
doi:10.23956/ijarcsse/v7i2/0133 fatcat:qsv637ma4nae3d7exwvp2cimte

The dynamics of polarized beliefs in networks governed by viral diffusion and media influence

Mohammad Reza Sanatkar
2020 Social Network Analysis and Mining  
In the third chapter, we consider the diffusion of polarized beliefs in a social network based on the influence of neighbors and the effect of mass media.  ...  (SC-LDPC) codes, estimation of the marginals of stationary distribution in influence networks consisting of a number of individuals with polarized beliefs, and estimation of per-node marginalized distribution  ...  The Dynamics of Polarized Beliefs in Networks Governed by Viral Diffusion and Media Influence In this chapter, we study the evolution of polarized beliefs governed by the intertwined dynamics of viral  ... 
doi:10.1007/s13278-020-0627-1 fatcat:cyq4fzroorbjlkcoe7yrhakyaq

Study of Residential Power Load Patterns Based on Clustering and Deep Belief Network

2017 Journal of Network Computing and Applications  
based on clustering and deep belief network is proposed.  ...  Firstly, use the improved k-means clustering algorithm for the residential electricity load clustering analysis to extract the typical load curve of each cell; and then a depth belief network classifier  ...  Deep Belief Network Classifier for Load Curve Classification The bottom of the deep belief network classifier (DBNC)is stacked by several layer Restricted Boltzmann Machine(RBM), and the top layer adds  ... 
doi:10.23977/jnca.2017.21002 fatcat:zbfulsrqfzatjop54z545svday

Best-First AND/OR Search for Most Probable Explanations [article]

Radu Marinescu, Rina Dechter
2012 arXiv   pre-print
networks.  ...  The paper evaluates the power of best-first search over AND/OR search spaces for solving the Most Probable Explanation (MPE) task in Bayesian networks.  ...  search algorithm for solving the MPE task in belief networks.  ... 
arXiv:1206.5268v1 fatcat:z4qd76htpfbpfiliatj7kuoge4

User Facilitated Congestion and Attack Mitigation [chapter]

Mürsel Yildiz, Ahmet Cihat Toker, Fikret Sivrikaya, Seyit Ahmet Camtepe, Sahin Albayrak
2012 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
In this work, we introduce an integrated solution to congestion avoidance and attack mitigation problems through cooperation among wireless access points.  ...  The IEEE Wireless LAN standard has been a true success story by enabling convenient, efficient and low-cost access to broadband networks for both private and professional use.  ...  Secondly, in Ok to Attack (O A ) state, data traffic check observation is done in addition to observed bad user network delay experience and bad SNMP check observation.  ... 
doi:10.1007/978-3-642-30422-4_26 fatcat:7eepvmfnjffxvkwhuwq2oow7ou

Network segregation in a model of misinformation and fact-checking

Marcella Tambuscio, Diego F. M. Oliveira, Giovanni Luca Ciampaglia, Giancarlo Ruffo
2018 Journal of Computational Social Science  
One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax.  ...  One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network.  ...  GLC acknowledges support from the Indiana University Network Science Institute (http:// and from the Swiss National Science Foundation (PBTIP2_142353).  ... 
doi:10.1007/s42001-018-0018-9 fatcat:actt24xoircslblxz2367lsfzy

Evolution of beliefs in social networks [article]

Pushpi Paranamana, Pei Wang, Patrick Shafto
2022 arXiv   pre-print
We analyze three cases: static network, randomly changing network, and homophily-based dynamic network.  ...  We prove that homophily-based network structures do not in general converge to a single set of beliefs shared by all and prove lower bounds on the number of different limiting beliefs as a function of  ...  Acknowledgements This research was supported in part by DARPA grant HR00112020039, NSF MRI 1828528, and NSF Inspire 1549981 to PS.  ... 
arXiv:2205.13587v1 fatcat:yqbdavpc7fgwbmbeklegkd5dhe

Learning an Unknown Network State in Routing Games [article]

Manxi Wu, Saurabh Amin
2019 arXiv   pre-print
We study learning dynamics induced by myopic travelers who repeatedly play a routing game on a transportation network with an unknown state.  ...  The state impacts cost functions of one or more edges of the network. In each stage, travelers choose their routes according to Wardrop equilibrium based on public belief of the state.  ...  Model In this section, we introduce our model of learning dynamics in which travelers repeatedly play a routing game in a transportation network.  ... 
arXiv:1905.04433v1 fatcat:wuuzi54gdbcf5kjgd6uh7w4czy
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