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94 results on '"Ozertem, Umut"'

1. Preserving Integrity in Online Social Networks

Author

Halevy, Alon, Ferrer, Cristian Canton, Ma, Hao, Ozertem, Umut, Pantel, Patrick, Saeidi, Marzieh, Silvestri, Fabrizio, and Stoyanov, Ves

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence
Abstract

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

Published
2020

2. Preserving Integrity in Online Social Networks: The recent progress and ongoing problems of keeping online platforms and their users safe from malicious activities.

Author

HALEVY, ALON, CANTON-FERRER, CRISTIAN, HAO MA, OZERTEM, UMUT, PANTEL, PATRICK, SAEIDI, MARZIEH, SILVESTRI, FABRIZIO, and STOYANOV, VES

Subjects
ONLINE social networks, INTEGRITY, TRUTHFULNESS & falsehood, FREEDOM of speech
Abstract

THE GOAL OF online social networks is to help create connections between people (online and offline), to connect people to communities of interest, and to provide a forum for advancing culture. Social networks advance these causes by providing a platform for free expression by anyone, whether they are well-known figures or your next-door neighbor. Unfortunately, open platforms for free expression can be used for malicious purposes. People and organizations can distribute misinformation and hate speech and can use the platform to commit crimes such as selling illegal drugs, coordinating sex trafficking, or child exploitation. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Nonlinear Coordinate Unfolding Via Principal Curve Projections with Application to Nonlinear BSS

Author

Erdogmus, Deniz, Ozertem, Umut, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ishikawa, Masumi, editor, Doya, Kenji, editor, Miyamoto, Hiroyuki, editor, and Yamakawa, Takeshi, editor

Published
2008
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5. Recursive Generalized Eigendecomposition for Independent Component Analysis

Author

Ozertem, Umut, Erdogmus, Deniz, Lan, Tian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rosca, Justinian, editor, Erdogmus, Deniz, editor, Príncipe, José C., editor, and Haykin, Simon, editor

Published
2006
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6. Preserving Integrity in Online Social Networks

Author

Halevy, Alon, Ferrer, Cristian Canton, Ma, Hao, Ozertem, Umut, Pantel, Patrick, Saeidi, Marzieh, Silvestri, Fabrizio, and Stoyanov, Ves

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Integrity, Artificial Intelligence (cs.AI), Fake News, Social Media, Facebook, Meta, Facebook AI, General Computer Science, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks
Abstract

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

Published
2022

8. Principal curve time warping

Author

Ozertem, Umut and Erdogmus, Deniz

Subjects
Kernel functions -- Usage, Time-series analysis -- Usage, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

9. Continuously differentiable sample-spacing entropy estimation

Author

Ozertem, Umut, Uysal, Ismail, and Erdogmus, Deniz

Subjects
Neural networks -- Design and construction, Entropy (Information theory) -- Measurement, Statistical methods -- Usage, Neural network, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The insufficiency of using only second-order statistics and premise of exploiting higher order statistics of the data has been well understood, and more advanced objectives including higher order statistics, especially those stemming from information theory, such as error entropy minimization, are now being studied and applied in many contexts of machine learning and signal processing. In the adaptive system training context, the main drawback of utilizing output error entropy as compared to correlation-estimation-based second-order statistics is the computational load of the entropy estimation, which is usually obtained via a plug-in kernel estimator. Sample-spacing estimates offer computationally inexpensive entropy estimators; however, resulting estimates are not differentiable, hence, not suitable for gradient-based adaptation. In this brief paper, we propose a nonparametric entropy estimator that captures the desirable properties of both approaches. The resulting estimator yields continuously differentiable estimates with a computational complexity at the order of those of the sample-spacing techniques. The proposed estimator is compared with the kernel density estimation (KDE)-based entropy estimator in the supervised neural network training framework with computation time and performance comparisons. Index Terms--Entropy estimation, minimum error entropy (MEE) criterion, supervised neural network training.

Published
2008

15. Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond

Author

He, Haibo, primary, Chawla, Nitesh, additional, Chen, Huanhuan, additional, Choe, Yoonsuck, additional, Engelbrecht, Andries, additional, deva, Jaya, additional, Long, Lyle, additional, Minai, Ali, additional, Nie, Feiping, additional, Ozertem, Umut, additional, Pearlmutter, Barak, additional, Shao, Ling, additional, Si, Jennie, additional, Steil, Jochen, additional, Verma, Brijesh, additional, and Wang, Ding, additional

Published
2016
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19. Mobile query reformulations

Author

Shokouhi, Milad, primary, Jones, Rosie, additional, Ozertem, Umut, additional, Raghunathan, Karthik, additional, and Diaz, Fernando, additional

Published
2014
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21. Learning to suggest

Author

Ozertem, Umut, primary, Chapelle, Olivier, additional, Donmez, Pinar, additional, and Velipasaoglu, Emre, additional

Published
2012
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28. A Study of Medication-Taking and Unobtrusive, Intelligent Reminding

Author

Hayes, Tamara L., primary, Cobbinah, Kofi, additional, Dishongh, Terry, additional, Kaye, Jeffrey A., additional, Kimel, Janna, additional, Labhard, Michael, additional, Leen, Todd, additional, Lundell, Jay, additional, Ozertem, Umut, additional, Pavel, Misha, additional, Philipose, Matthai, additional, Rhodes, Kevin, additional, and Vurgun, Sengul, additional

Published
2009
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49. Information Theoretic Feature Selection and Projection.

Author

Kacprzyk, Janusz, Prasad, Bhanu, Prasanna, S. R. Mahadeva, Erdogmus, Deniz, Ozertem, Umut, and Lan, Tian

Abstract

Pattern recognition systems are trained using a finite number of samples. Feature dimensionality reduction is essential to improve generalization and optimal exploitation of the information content in the feature vector. Dimensionality reduction eliminates redundant dimensions that do not convey reliable statistical information for classification, determines a manifold on which projections of the original high- dimensional feature vector exhibit maximal information about the class label, and reduces the complexity of the classifier to help avoid over-fitting. In this chapter we demonstrate computationally efficient algorithms for estimating and optimizing mutual information, specifically for the purpose of learning optimal feature dimensionality reduction solutions in the context of pattern recognition. These techniques and algorithms will be applied to the classification of multichannel EEG signals for brain computer interface design, as well as sonar imagery for target detection. Results will be compared with widely used benchmark alternatives such as LDA and kernel LDA. [ABSTRACT FROM AUTHOR]

Published
2008
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50. Recursive Generalized Eigendecomposition for Independent Component Analysis.

Author

Rosca, Justinian, Príncipe, José C., Haykin, Simon, Ozertem, Umut, Erdogmus, Deniz, and Tian Lan

Abstract

Independent component analysis is an important statistical tool in machine learning, pattern recognition, and signal processing. Most of these applications require on-line learning algorithms. Current on-line ICA algorithms use the stochastic gradient concept, drawbacks of which include difficulties in selecting the step size and generating suboptimal estimates. In this paper a recursive generalized eigendecomposition algorithm is proposed that tracks the optimal solution that one would obtain using all the data observed. [ABSTRACT FROM AUTHOR]

Published
2006
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