Institution
Georgia Institute of Technology
Education•Atlanta, Georgia, United States•
About: Georgia Institute of Technology is a education organization based out in Atlanta, Georgia, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 45387 authors who have published 119086 publications receiving 4651220 citations.
Topics: Population, Computer science, Nonlinear system, Context (language use), Finite element method
Papers published on a yearly basis
Papers
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TL;DR: This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture that tends to achieve significant improvements in terms of various objective quality measures.
Abstract: This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be separated from the background noises successfully without the annoying musical artifact commonly observed in conventional speech enhancement algorithms. A series of pilot experiments were conducted under multi-condition training with more than 100 hours of simulated speech data, resulting in a good generalization capability even in mismatched testing conditions. When compared with the logarithmic minimum mean square error approach, the proposed DNN-based algorithm tends to achieve significant improvements in terms of various objective quality measures. Furthermore, in a subjective preference evaluation with 10 listeners, 76.35% of the subjects were found to prefer DNN-based enhanced speech to that obtained with other conventional technique.
860 citations
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01 Jan 2008TL;DR: This paper proposes an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C &C server addresses, and shows that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate.
Abstract: Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate.
859 citations
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TL;DR: This paper systematically outline the optimization challenges that arise when developing technology to support ride-sharing and survey the related operations research models in the academic literature.
858 citations
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16 Apr 2009TL;DR: The notion of order-preserving symmetric encryption (OPE) was introduced by Agrawal et al. as mentioned in this paper, who showed that a straightforward relaxation of standard security notions for encryption such as indistinguishability against chosen-plaintext attack (IND-CPA) is unachievable by a practical OPE scheme.
Abstract: We initiate the cryptographic study of order-preserving symmetric encryption (OPE), a primitive suggested in the database community by Agrawal et al. (SIGMOD '04) for allowing efficient range queries on encrypted data. Interestingly, we first show that a straightforward relaxation of standard security notions for encryption such as indistinguishability against chosen-plaintext attack (IND-CPA) is unachievable by a practical OPE scheme. Instead, we propose a security notion in the spirit of pseudorandom functions (PRFs) and related primitives asking that an OPE scheme look "as-random-as-possible" subject to the order-preserving constraint. We then design an efficient OPE scheme and prove its security under our notion based on pseudorandomness of an underlying blockcipher. Our construction is based on a natural relation we uncover between a random order-preserving function and the hypergeometric probability distribution. In particular, it makes black-box use of an efficient sampling algorithm for the latter.
858 citations
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01 Jan 1973TL;DR: In this paper, experimental and theoretical aspects of the mobility and diffusion of ions in gases are studied in detail, including ion-ion interaction, boundary condition and ion and electron behavior, and the problems of the diffusion coefficients and the afterglow techniques.
Abstract: Experimental and theoretical aspects of the mobility and diffusion of ions in gases are studied in detail. Some of the subjects discussed include ion-ion interaction, boundary condition and ion and electron behavior. Also discussed in separate chapters are the problems of the diffusion coefficients and the afterglow techniques. Finally, a special chapter studies the kinetic theory of diffusion and mobility, stressing the low-, medium- and high-field theory.
858 citations
Authors
Showing all 45752 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Younan Xia | 216 | 943 | 175757 |
Paul M. Thompson | 183 | 2271 | 146736 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Jiawei Han | 168 | 1233 | 143427 |
John H. Seinfeld | 165 | 921 | 114911 |
David J. Mooney | 156 | 695 | 94172 |
Richard E. Smalley | 153 | 494 | 111117 |
Vivek Sharma | 150 | 3030 | 136228 |
James M. Tiedje | 150 | 688 | 102287 |
Philip S. Yu | 148 | 1914 | 107374 |
Kevin Murphy | 146 | 728 | 120475 |
Gordon T. Richards | 144 | 613 | 110666 |
Yi Yang | 143 | 2456 | 92268 |
Joseph T. Hupp | 141 | 731 | 82647 |