String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Non-volatile memory device having vertical structure and method of operating the same

Abstract: A non-volatile memory device having a vertical structure includes a NAND string having a vertical structure. The NAND string includes a plurality of memory cells, and at least one pair of first selection transistors arranged to be adjacent to a first end of the plurality of memory cells. A plurality of word lines are coupled to the plurality of memory cells of the NAND string. A first selection line is commonly connected to the at least one pair of first selection transistors of the NAND string.

System and method for finding desired results by incremental search using an ambiguous keypad with the input containing orthographic and typographic errors

Abstract: A system for finding and presenting content items in response to keystrokes entered by a user on an input device having a known layout of overloaded keys selected from a set of key layouts. The system includes (1) a database containing content items and terms characterizing the content items; (2) input logic for receiving keystrokes from the user and building a string corresponding to incremental entries by the user, each item in the string having the set of alphanumeric symbols associated with a corresponding keystroke; (3) mapping logic to map the string to the database to find the most likely content items corresponding to the incremental entries, the mapping logic operating in accordance with a defined error model corresponding to the known layout of overloaded keys; and (4) presentation logic for ordering the most likely content items identified by the mapping logic and for presenting the most likely content items.

On deriving unknown vulnerabilities from zero-day polymorphic and metamorphic worm exploits

Abstract: Vulnerabilities that allow worms to hijack the control flow of each host that they spread to are typically discovered months before the worm outbreak, but are also typically discovered by third party researchers. A determined attacker could discover vulnerabilities as easily and create zero-day worms for vulnerabilities unknown to network defenses. It is important for an analysis tool to be able to generalize from a new exploit observed and derive protection for the vulnerability.Many researchers have observed that certain predicates of the exploit vector must be present for the exploit to work and that therefore these predicates place a limit on the amount of polymorphism and metamorphism available to the attacker. We formalize this idea and subject it to quantitative analysis with a symbolic execution tool called DACODA. Using DACODA we provide an empirical analysis of 14 exploits (seven of them actual worms or attacks from the Internet, caught by Minos with no prior knowledge of the vulnerabilities and no false positives observed over a period of six months) for four operating systems.Evaluation of our results in the light of these two models leads us to conclude that 1) single contiguous byte string signatures are not effective for content filtering, and token-based byte string signatures composed of smaller substrings are only semantically rich enough to be effective for content filtering if the vulnerability lies in a part of a protocol that is not commonly used, and that 2) practical exploit analysis must account for multiple processes, multithreading, and kernel processing of network data necessitating a focus on primitives instead of vulnerabilities.

Speech synthesis with prosodic model data and accent type

Abstract: A speech synthesizing method includes determining the accent type of the input character string, selecting the prosodic model data from a prosody dictionary for storing typical ones of the prosodic models representing the prosodic information for the character strings in a word dictionary, based on the input character string and the accent type, transforming the prosodic information of the prosodic model when the character string of the selected prosodic model is not coincident with the input character string, selecting the waveform data corresponding to each character of the input character string from a waveform dictionary, based on the prosodic model data after transformation, and connecting the selected waveform data with each other. Therefore, a difference between an input character string and a character string stored in a dictionary is absorbed, then it is possible to synthesize a natural voice.

String matching in Lempel-Ziv compressed strings

Abstract: String matching and compression are two widely studied areas of computer science. The theory of string matching has a long association with compression algorithms. Data structures from string matching can be used to derive fast implementations of many important compression schemes, most notably the Lempel—Ziv (LZ77) algorithm. Intuitively, once a string has been compressed—and therefore its repetitive nature has been elucidated—one might be tempted to exploit this knowledge to speed up string matching. The Compressed Matching Problem is that of performing string matching in a compressed text, without uncompressing it. More formally, let T be a text, let Z be the compressed string representing T , and let P be a pattern. The Compressed Matching Problem is that of deciding if P occurs in T , given only P and Z . Compressed matching algorithms have been given for several compression schemes such as LZW. In this paper we give the first nontrivial compressed matching algorithm for the classic adaptive compression scheme, the LZ77 algorithm. In practice, the LZ77 algorithm is known to compress more than other dictionary compression schemes, such as LZ78 and LZW, though for strings with constant per bit entropy, all these schemes compress optimally in the limit. However, for strings with o(1) per bit entropy, while it was recently shown that the LZ77 gives compression to within a constant factor of optimal, schemes such as LZ78 and LZW may deviate from optimality by an exponential factor. Asymptotically, compressed matching is only relevant if |Z|=o(|T|) , i.e., if the compression ratio |T|/|Z| is more than a constant. These results show that LZ77 is the appropriate compression method in such settings. We present an LZ77 compressed matching algorithm which runs in time O(n log 2 u/n + p) where n=|Z| , u=|T| , and p=|P| . Compare with the naive ``decompresion'' algorithm, which takes time Θ(u+p) to decide if P occurs in T . Writing u+p as (n u)/n+p , we see that we have improved the complexity, replacing the compression factor u/n by a factor log 2 u/n . Our algorithm is competitive in the sense that O(n log 2 u/n + p)=O(u+p) , and opportunistic in the sense that O(n log 2 u/n + p)=o(u+p) if n=o(u) and p=o(u) .