Institution
IBM
Company•Armonk, New York, United States•
About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors take a critical look at the relationship between the security of cryptographic schemes in the Random Oracle Model, and the schemes that result from implementing the random oracle by so called "cryptographic hash functions".
Abstract: We take a critical look at the relationship between the security of cryptographic schemes in the Random Oracle Model, and the security of the schemes that result from implementing the random oracle by so called "cryptographic hash functions". The main result of this paper is a negative one: There exist signature and encryption schemes that are secure in the Random Oracle Model, but for which any implementation of the random oracle results in insecure schemes.
In the process of devising the above schemes, we consider possible definitions for the notion of a "good implementation" of a random oracle, pointing out limitations and challenges.
1,007 citations
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09 May 1994TL;DR: A slightly enhanced scheme is shown to have the property that the adversary can create ciphertexts only of strings for which she “knows” the corresponding plaintexts—such a scheme is not only semantically secure but also non-malleable and secure against chosen-ciphertext attack.
Abstract: Given an arbitrary k-bit to k-bit trapdoor permutation f and a hash function, we exhibit an encryption scheme for which (i) any string x of length slightly less than k bits can be encrypted as f(rx), where r x is a simple probabilistic encoding of x depending on the hash function; and (ii) the scheme can be proven semantically secure assuming the hash function is “ideal.” Moreover, a slightly enhanced scheme is shown to have the property that the adversary can create ciphertexts only of strings for which she “knows” the corresponding plaintexts—such a scheme is not only semantically secure but also non-malleable and secure against chosen-ciphertext attack.
1,007 citations
IBM1
TL;DR: Damascene copper electroplating for on-chip interconnections, a process that was conceived and developed in the early 1990s, makes it possible to fill submicron trenches and vias with copper without creating a void or a seam and has thus proven superior to other technologies of copper deposition as discussed by the authors.
Abstract: Damascene copper electroplating for on-chip interconnections, a process that we conceived and developed in the early 1990s, makes it possible to fill submicron trenches and vias with copper without creating a void or a seam and has thus proven superior to other technologies of copper deposition. We discuss here the relationship of additives in the plating bath to superfilling, the phenomenon that results in superconformal coverage, and we present a numerical model which accounts for the experimentally observed profile evolution of the plated metal.
1,006 citations
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IBM1
TL;DR: Digital Testing and the Need for Testable design Principles of Testable Design Pseudorandom Sequence Generators Test Response Compression Techniques and Limitations and Other Concerns of Random Pattern Testing Test System Requirements for Built-In Test Appendix References Index.
Abstract: Digital Testing and the Need for Testable Design Principles of Testable Design Pseudorandom Sequence Generators Test Response Compression Techniques Shift-Register Polynomial Division Special-Purpose Shift-Register Circuits Random Pattern Built-In Test Built-In Test Structures Limitations and Other Concerns of Random Pattern Testing Test System Requirements for Built-In Test Appendix References Index.
1,004 citations
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IBM1
TL;DR: SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Abstract: We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.
1,002 citations
Authors
Showing all 134658 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Anil K. Jain | 183 | 1016 | 192151 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Rodney S. Ruoff | 164 | 666 | 194902 |
Tobin J. Marks | 159 | 1621 | 111604 |
Jean M. J. Fréchet | 154 | 726 | 90295 |
Albert-László Barabási | 152 | 438 | 200119 |
György Buzsáki | 150 | 446 | 96433 |
Stanislas Dehaene | 149 | 456 | 86539 |
Philip S. Yu | 148 | 1914 | 107374 |
James M. Tour | 143 | 859 | 91364 |
Thomas P. Russell | 141 | 1012 | 80055 |
Naomi J. Halas | 140 | 435 | 82040 |
Steven G. Louie | 137 | 777 | 88794 |
Daphne Koller | 135 | 367 | 71073 |