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.

Recognition unit model training based on competing word and word string models

Abstract: A system pattern-based speech recognition, e.g., a hidden Markov model (HMM) based speech recognizer using Viterbi scoring. The principle of minimum recognition error rate is applied by the present invention using discriminative training. Various issues related to the special structure of HMMs are presented. Parameter update expressions for HMMs are provided.

System and method for monitoring photovoltaic power generation systems

Abstract: A system and method for monitoring photovoltaic power generation systems or arrays (230), both on a local (site) level (100) and from a central location (610). The system includes panel and string combiner sentries (70) or intelligent devices, in bidirectional communication with a master device on the site to facilitate installation and troubleshooting of faults in the array (e.g., Fig. 9), including performance monitoring and diagnostic data collection (e.g., Figs. 14, 15).

Suffix arrays: a new method for on-line string searches

Abstract: A new and conceptually simple data structure, called a suffixarray, for on-line string searches is introduced in this paper. Constructing and querying suffixarrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffixarrays over suffixtrees is that, in practice, they use three to five times less space. From a complexity standpoint, suffix arrays permit on-line string searches of the type, ‘‘Is W a substring of A?’’ to be answered in time O(P + log N), where P is the length of W and N is the length of A, which is competitive with (and in some cases slightly better than) suffixtrees. The only drawback is that in those instances where the underlying alphabet is finiteand small, suffixtrees can be constructed in O(N) time in the worst case, versus O(N log N) time for suffixarrays. However, we give an augmented algorithm that, regardless of the alphabet size, constructs suffixarrays in O(N) expected time, albeit with lesser space efficiency. We believe that suffixarrays will prove to be better in practice than suffixtrees for many applications.

System for chinese tokenization and named entity recognition

Abstract: A system (100, 200) for tokenization and named entity recognition of ideographic language is disclosed In the system, a word lattice is generated for a string of ideographic characters using finite state grammars (150) and a system lexicon (240) Segmented text is generated by determining word boundaries in the string of ideographic characters using the word lattice dependent upon a contextual language model (152A) and one or more entity language models (152B) One or more named entities is recognized in the string of ideographic characters using the word lattice dependent upon the contextual language model (152A) and the one or more entity language models (152B) The contextual language model (152A) and the one or more entity language models (152B) are each class-based language models The lexicon (240) includes single ideographic characters, words , and predetermined features of the characters and words

Sonic: Zero-Knowledge SNARKs from Linear-Size Universal and Updatable Structured Reference Strings

Abstract: Ever since their introduction, zero-knowledge proofs have become an important tool for addressing privacy and scalability concerns in a variety of applications. In many systems each client downloads and verifies every new proof, and so proofs must be small and cheap to verify. The most practical schemes require either a trusted setup, as in (pre-processing) zk-SNARKs, or verification complexity that scales linearly with the complexity of the relation, as in Bulletproofs. The structured reference strings required by most zk-SNARK schemes can be constructed with multi-party computation protocols, but the resulting parameters are specific to an individual relation. Groth et al. discovered a zk-SNARK protocol with a universal structured reference string that is also updatable, but the string scales quadratically in the size of the supported relations. Here we describe a zero-knowledge SNARK, Sonic, which supports a universal and continually updatable structured reference string that scales linearly in size. We also describe a generally useful technique in which untrusted "helpers" can compute advice that allows batches of proofs to be verified more efficiently. Sonic proofs are constant size, and in the "helped" batch verification context the marginal cost of verification is comparable with the most efficient SNARKs in the literature.