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A Novel Probabilistic Data Flow Framework [chapter]

Eduard Mehofer, Bernhard Scholz
2001 Lecture Notes in Computer Science  
In this paper we develop a novel, practicable framework for probabilistic data flow problems.  ...  Probabilistic data flow systems compute a range, i.e. a probability, with which a data flow fact will hold at some program point.  ...  Ramalingam [13] presents a generic data flow framework which computes the probability that a data flow fact will hold at some program point for finite  ... 
doi:10.1007/3-540-45306-7_4 fatcat:jpzgqh7v2vhitndkc3jfvm4dny

Message from the Guest Editors

Sushil Jajodia, Jianying Zhou
2011 International Journal of Information Security  
When SAS processes packets in a suspicious flow pool, it uses data flow analysis techniques to remove non-critical bytes and then applies a hidden Markov model (HMM) to the refined data to generate state-transitiongraph-based  ...  In the paper "CASSANDRA: A Probabilistic, Efficient, and Privacy Preserving Solution to Compute Set Intersection," Marconi et al. propose a toolbox composed of three probabilistic protocols that allow  ...  When SAS processes packets in a suspicious flow pool, it uses data flow analysis techniques to remove non-critical bytes and then applies a hidden Markov model (HMM) to the refined data to generate state-transitiongraph-based  ... 
doi:10.1007/s10207-011-0131-8 fatcat:bgjik2tlnrghppxzjyhzffoa7i

Quantitative Security Analysis (Dagstuhl Seminar 12481)

Boris Köpf, Paquale Malacaria, Catuscia Palamidessi, Marc Herbstritt
2013 Dagstuhl Reports  
The high amount of trust put into today's software systems calls for a rigorous analysis of their security.  ...  Under such constraints, the relevant question is not whether a system is secure, but rather how much security it provides.  ...  This requires data flow-tracking concepts and capabilities in data usage control frameworks.  ... 
doi:10.4230/dagrep.2.11.135 dblp:journals/dagstuhl-reports/KopfMP12 fatcat:zlrdsjynqnfv3ehvlybivdnwre

Dynamic Probabilistic Volumetric Models

Ali Osman Ulusoy, Octavian Biris, Joseph L. Mundy
2013 2013 IEEE International Conference on Computer Vision  
This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes.  ...  A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces.  ...  Novel view rendering and 3-d tracking applications are used to demonstrate the high quality of 4-d data learned from imagery as well as the benefit of dense space time data for flow analysis.  ... 
doi:10.1109/iccv.2013.68 dblp:conf/iccv/UlusoyBM13 fatcat:tkncuqjzj5hv5ahwcrpbxjiguy

SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds [article]

Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim
2020 arXiv   pre-print
In this paper, we propose SoftFlow, a probabilistic framework for training normalizing flows on manifolds.  ...  To sidestep the dimension mismatch problem, SoftFlow estimates a conditional distribution of the perturbed input data instead of learning the data distribution directly.  ...  Conclusion In this paper, we have introduced a novel probabilistic framework, SoftFlow, for training a normalizing flow on manifolds.  ... 
arXiv:2006.04604v4 fatcat:qa4nn4phtbgfbmqq4uuacnacr4

Survey on Cloud Based IP Traceback Authentication Framework

Aswathy T, Misha Ravi
2018 International Journal Of Engineering And Computer Science  
A time limited token based authentication framework for authenticating traceback service queries is implemented.  ...  The design objective of the framework is preventing the illegal users for accessing traceback information.Thus to prevent network traffic attack  ...  In the cloud based authentication framework, user gets a token that have a specific time for accessing traceback service.  ... 
doi:10.18535/ijecs/v7i3.10 fatcat:23jbq4hohngm5dpbvqakc52ni4

Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs [article]

Wei Ma, Sean Qian
2022 arXiv   pre-print
With the availability of massive traffic data and the emergence of advanced computational methods, this paper develops a data-driven framework that solves the probabilistic dynamic origin-destination demand  ...  Overall, the developed PDODE framework provides a practical tool for public agencies to understand the sources of demand stochasticity, evaluate day-to-day variation of network flow, and make reliable  ...  In this paper, we develop a data-driven framework that solves the probabilistic dynamic OD demand estimation (PDODE) problem using multi-day traffic data on general networks.  ... 
arXiv:2204.09229v1 fatcat:qj5utsgf7bdhzomekxyyyazkbm

A Novel Probabilistic Framework to Study the Impact of PV-battery Systems on Low-Voltage Distribution Networks [article]

Yiju Ma, Donald Azuatalam, Thomas Power, Gregor Verbic, Archie C. Chapman
2019 arXiv   pre-print
To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks.  ...  Specifically, the framework incorporates home energy management(HEM) operational decisions within the MC time series power flow analysis.  ...  Furthermore, the proposed framework allows MC power flow analysis to be conducted with exiguous smart meter data.  ... 
arXiv:1809.07488v2 fatcat:alx3oiw6gnhkzoyiu5pndmnrl4

Geodesic-based probability propagation for efficient optical flow

Ling Mei, Zeyu Chen, Jianhuang Lai
2018 Electronics Letters  
To achieve efficient belief propagation, a probabilistic framework for optimising the Markov random field (MRF) objective is proposed.  ...  In this way, the limitation of local propagation can be tackled in the global image level, and the probabilistic framework reduces computational complexity in the optimisation.  ...  Secondly, as an alternative to the message and belief propagation, we formulate the optimisation problem in a probabilistic framework.  ... 
doi:10.1049/el.2018.0394 fatcat:z6a3rz77zjc3fgyq3cndhfwulu

Complete multi-view reconstruction of dynamic scenes from probabilistic fusion of narrow and wide baseline stereo

Tony Tung, Shohei Nobuhara, Takashi Matsuyama
2009 2009 IEEE 12th International Conference on Computer Vision  
In particular we present an original probabilistic framework to derive and predict the true surface of models.  ...  This paper presents a novel approach to achieve accurate and complete multi-view reconstruction of dynamic scenes (or 3D videos). 3D videos consist in sequences of 3D models in motion captured by a surrounding  ...  The fusion is formulated in a novel Bayesian probabilistic framework to estimate the true surface of models. Finally, a min-cut problem is formulated and efficiently solved using graph-cuts.  ... 
doi:10.1109/iccv.2009.5459384 dblp:conf/iccv/TungNM09 fatcat:s5543fddybbopm72pvqo6ruqrm

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Wei Ma, Zhen (Sean) Qian
2018 Transportation Research Part C: Emerging Technologies  
Making the best use of day-to-day traffic data collected over many years, this paper develops a novel theoretical framework for estimating the mean and variance/covariance matrix of O-D demand considering  ...  Recent transportation network studies on uncertainty and reliability call for modeling the probabilistic O-D demand and probabilistic network flow.  ...  Acknowledgement This research is funded in part by Traffic 21 Institute and Carnegie Mellon University's Mobility21, a National University Transportation Center for Mobility sponsored by the US Department  ... 
doi:10.1016/j.trc.2017.12.015 fatcat:5mjshld2ancitcbixtpqdxs5je

Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes [article]

Fabian Brickwedde, Steffen Abraham, Rudolf Mester
2019 arXiv   pre-print
These methods are traditionally based on a temporal series of stereo images. In this paper, we propose a novel monocular 3D scene flow estimation method, called Mono-SF.  ...  Existing 3D scene flow estimation methods provide the 3D geometry and 3D motion of a scene and gain a lot of interest, for example in the context of autonomous driving.  ...  Method The monocular scene flow estimation method, Mono-SF, is designed to combine multi-view geometry with probabilistic single-view depth information in a probabilistic optimization framework.  ... 
arXiv:1908.06316v1 fatcat:7rn4uahxtvfbbbf7e3nx5fizwi

A Framework for Distributed Managing Uncertain Data in RFID Traceability Networks [chapter]

Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah, Yanbo Wu
2012 Lecture Notes in Computer Science  
In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks.  ...  The framework consists of a global object tracking model and a local RFID data cleaning model.  ...  We have designed and implemented a novel framework, which improves the existing techniques for tracking the movement of objects and cleaning RFID raw data.  ... 
doi:10.1007/978-3-642-35063-4_22 fatcat:mysej4uqjjbzrhwigqu4ycppvu

End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

Jun-Yi Hang, Min-Ling Zhang, Yanghe Feng, Xiaocheng Song
2022 AAAI Conference on Artificial Intelligence  
To instantiate it, we propose modelling the prototypes probabilistically by the normalizing flows, which possess adaptive prototypical complexity to fully capture the underlying properties of each class  ...  In this paper, we make a first attempt towards a unified framework for prototype-based label-specific feature transformation, where the prototypes and the labelspecific features are directly optimized  ...  We thank the Big Data Center of Southeast University for providing the facility support on the numerical calculations in this paper.  ... 
dblp:conf/aaai/HangZFS22 fatcat:lr4z7y4uvzhjlprs7sd466rxdm

Kognitor: Big Data Real-Time Reasoning and Probabilistic Programming

Arinze Anikwue, Boniface Kabaso
2021 International Journal on Data Science and Technology  
This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture.  ...  Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable  ...  The flow of data into Kognitor is managed by the feeder component. Data from multiple sources can be aggregated in the feeder component.  ... 
doi:10.11648/j.ijdst.20210702.12 fatcat:7qaf6rnotrfzlokoa4a54sm4nu
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