Length Of Input String Research Articles

An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network

Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.

Efficient and Precise Secure Generalized Edit Distance and Beyond.

Secure string-comparison by some non-linear metrics such as edit-distance and its variations is an important building block of many applications including patient genome matching and text-based intrusion detection. Despite the significance of these string metrics, computing them in a provably secure manner is very expensive. In this paper, we improve the performance of secure computation of these string metrics without sacrificing security, generality, composability, and accuracy. We explore a new design methodology that allows us to reduce the asymptotic cost by a factor of O(log n) (where n denotes the input string length). In our experiments, we observe up to an order-of-magnitude savings in time and bandwidth compared to the best prior results. We extended our semi-honest protocols to work in the malicious model, which is by-far the most efficient actively-secure protocols for computing these string metrics.

A faster implementation of online RLBWT and its application to LZ77 parsing

Run-length encoded Burrows–Wheeler Transformed strings, resulting in Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive strings. We propose a new algorithm for online RLBWT working in run-compressed space, which runs in O(nlg⁡r) time and O(rlg⁡n) bits of space, where n is the length of input string S received so far and r is the number of runs in the BWT of the reversed S. We improve the state-of-the-art algorithm for online RLBWT in terms of empirical construction time. Adopting the dynamic list for maintaining a total order, we can replace rank queries in a dynamic wavelet tree on a run-length compressed string by the direct comparison of labels in a dynamic list. Enlisting the proposed online RLBWT, we can efficiently compute the LZ77 factorization in run-compressed space. The empirical results show the efficiencies of both our online RLBWT and LZ77 parsing, especially for highly repetitive strings.

Parallel Hardware Stochastic Context-Free Parsers

In this paper a platform is presented, that given a stochastic context-free grammar (SCFG), automatically outputs the description of the parser in synthesizable hardware description language (HDL) which can be downloaded in an Field Programmable Gate Arrays (FPGA) board. Initially, according to our methodology the SCFG is augmented with attributes which store the probability values and can be evaluated through corresponding stack actions. The architecture of the produced system is based on a proposed extension of Earley’s parallel algorithm, which given an input string, generates the parse trees in the form of an AND-Or parse tree. This AND-or parse tree is then traversed using a proposed tree traversal technique in order to execute the corresponding actions in the correct order, so as to compute the necessary probabilities. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The parser generated by the presented platform has been tested for various SCFGs and compared to software approaches. The performance comparison is one to two orders of magnitude in favor of the presented hardware, compared to previous software approaches, depending on the application, the input string length and the number of produced trees.

TDAS: a touch dynamics based multi-factor authentication solution for mobile devices

Purpose The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.) is becoming very common. These mobile electronic services typically use a knowledge-based authentication method to authenticate a user (claimed identity). However, this authentication method is vulnerable to several security attacks. To counter the attacks and to make the authentication process more secure, this paper aims to investigate the use of touch dynamics biometrics in conjunction with a personal identification number (PIN)-based authentication method, and demonstrate its benefits in terms of strengthening the security of authentication services for mobile devices. Design/methodology/approach The investigation has made use of three light-weighted matching functions and a comprehensive reference data set collected from 150 subjects. Findings The investigative results show that, with this multi-factor authentication approach, even when the PIN is exposed, as much as nine out of ten impersonation attempts can be successfully identified. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length. Originality/value The novel contributions of this paper are twofold. Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the data set may be used to strengthen the protection of resources that are accessible via mobile devices.

Repairing data through regular expressions

Since regular expressions are often used to detect errors in sequences such as strings or date, it is natural to use them for data repair. Motivated by this, we propose a data repair method based on regular expression to make the input sequence data obey the given regular expression with minimal revision cost. The proposed method contains two steps, sequence repair and token value repair. For sequence repair, we propose the Regular-expression-based Structural Repair (RSR in short) algorithm. RSR algorithm is a dynamic programming algorithm that utilizes Nondeterministic Finite Automata (NFA) to calculate the edit distance between a prefix of the input string and a partial pattern regular expression with time complexity of O ( nm 2 ) and space complexity of O ( mn ) where m is the edge number of NFA and n is the input string length. We also develop an optimization strategy to achieve higher performance for long strings. For token value repair, we combine the edit-distance-based method and associate rules by a unified argument for the selection of the proper method. Experimental results on both real and synthetic data show that the proposed method could repair the data effectively and efficiently.

A platform for the automatic generation of attribute evaluation hardware systems

Attribute grammars (AG) allow the addition of context-sensitive properties into context free grammars, augmenting their expressional capabilities by using syntactic and semantic notations, making them in this way a really useful tool for a considerable number of applications. AGs have extensively been utilized in applications such as artificial intelligence, structural pattern recognition, compiler construction and even text editing. Obviously, the performance of an attribute evaluation system resides in the efficiency of the syntactic and semantic subsystems. In this paper, a hardware architecture for an attribute evaluation system is presented, which is based on an efficient combinatorial implementation of Earley's parallel parsing algorithm for the syntax part of the attribute grammar. The semantic part is managed by a special purpose module that traverses the parse tree and evaluates the attributes based on a proposed stack-based approach. The entire system is described in Verilog HDL (hardware design language), in a template form that given the specification of an arbitrary attribute grammar, the HDL synthesizable source code of the system is produced on the fly by a proposed automated tool. The generated code has been simulated for validation, synthesized and tested on an Xilinx FPGA (field programmable gate arrays) board for various AGs. Our method increases the performance up to three orders of magnitude compared to previous approaches, depending on the implementation, the size of the grammar and the input string length. This makes it particularly appealing for applications where attribute evaluation is a crucial aspect, like in real-time and embedded systems. Specifically, a natural language interface is presented, based on a question-answering application from the area of airline flights.

Research on Characteristics of Audio DAC Sigma-Delta Modulator on Field Programmable Gate Array

The original architectural scheme of a designed audio Sigma Delta DAC on the FPGA whose application in a computer sound system allows refusing of an analogue sound track and improving the sound quality is presented. Modelling results of the audio interpolator filters transmission characteristics and the Sigma Delta modulator’s quantization noise parameters are given. It was determined that requirements are met best by a CIC filter amplitude frequency characteristics when the interpolation multiplier is L=8, the number of stages is N = 6, the input string length is 16 bits, the output string length is 31 bits, and the discretization frequency is 2.8224 MHz. The results of the modulator modelling showed that when a sound signal frequency band is of 20 kHz the modulator’s effective number of bits is 16, while the quantization noise power is minus 118dB. The modelling results are also proven by the experimental research when a DAC has been realized with Xilinx Spartan XC3S400-4PQ208 on the array. Ill. 12, bibl. 4 (in English; summaries in English, Russian and Lithuanian).

Efficient reconfigurable embedded parsers

This paper presents a highly efficient architecture for the hardware implementation of context-free grammar (CFG) parsers. Its efficiency stems from an innovative combinatorial circuit that implements the fundamental operation of Earley's parsing algorithm in time complexity O ( log 2 | G | ) , where | G | is the size of the CFG. Using this hardware architecture in a template form, we have developed an automated synthesis tool that, given the specification of an arbitrary CFG, generates the HDL (Hardware Design Language) synthesizable source code of the hardware parser for the given grammar. The generated source has been simulated for validation, synthesized and tested on a Xilinx FPGA (Field Programmable Gate Array) board. Our method increases the performance by a factor of one to two orders of magnitude, compared to previous hardware implementations, depending on the size of the grammar and the input string length. The speedup, compared to the pure software implementation, varies from two orders of magnitude for toy-scale grammars to six orders of magnitude for large real life grammars. This makes it particularly appealing for applications where (syntactic) pattern recognition response is a crucial aspect.

Construction and evaluation of language models based on stochastic context‐free grammar for speech recognition

AbstractThis paper deals with the use of a stochastic context‐free grammar (SCFG) for large vocabulary continuous speech recognition; in particular, an SCFG with phrase‐level dependency rules is built. Unlike n‐gram models, the SCFG can describe not only local constraints but also global constraints pertaining to the sentence as a whole, thus making possible language models with great expressive power. However, the inside‐outside algorithm must be used for estimation of the SCFG parameters, which involves a great amount of calculation, proportional to the third power of the number of nonterminal symbols and of the input string length. Hence, due to problems in dealing with extensive text corpora, the SCFG has hardly been applied as a language model for very large vocabulary continuous speech recognition. The proposed phrase‐level dependency SCFG allows a significant reduction of the computational load. In experiments with the EDR corpus, the proposed method proved effective. In experiments with the Mainichi corpus, a large‐scale phrase‐level dependency SCFG was built for a very large vocabulary continuous speech recognition system. Speech recognition tests with a vocabulary of about 5000 words showed that the proposed method could not compare with the trigram model in performance; however, when it was used in combination with a trigram model, the error rate was reduced by 14% compared to the trigram model alone. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(13): 48–59, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1172

Proposal of partially decodable Ziv-Lempel code

With the digital representation of various data, such as speech, images, and messages, together with enhanced computer capabilities, it has become indispensable to apply data compression techniques to communication systems and recording systems. Among those, universal codes, including Ziv-Lempel codes, are widely used in computers, since the best compression can be realized asymptotically when the input string length is increased, even if the statistical properties of the input sequence are unknown. A problem arises, however, in connection with random data access or browsing, which are important functions in multimedia systems. In the Ziv-Lempel code, decoding must always be started from the top of the codeword sequence, even if only a part of the input coded sequence is to be decoded. This leads to an excessive load on computer resources. This paper proposes a code based on the Ziv-Lempel code in which partial decoding is possible. The proposed code is theoretically analyzed. The condition for the proposed code to be a universal code is presented. The encoding and decoding algorithms are implemented on a computer, and the result of the analysis is shown to be valid. It is shown that the proposed code has advantages over the Ziv-Lempel code from the viewpoint of applications, because the range of error propagation is restricted when an error is produced in decoding, and the decoding process can be accelerated by parallel processing. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(12): 79–87, 1999

Algorithm partition and parallel recognition of general context-free languages using fixed-size VLSI architecture

Recognition of general context-free languages has been very important in language processing and syntactic pattern recognition. Many researchers have attempted to speed-up the recognition procedure. Recently, several VLSI implementations of context-free language recognition have been proposed. The main disadvantage of these proposed VLSI systems is that the size of the processor array is critical to the performance. That is, an n × n upper triangular array can only process strings with length not longer than n. In this paper we propose two algorithms which can recognize general context-free languages without restriction on the length of input string. The first one has time complexity O( n 4 k 2 ) using k × k processing elements to recognize n input symbols; if k = n, its time complexity will be O( n 2). The second one, if using k × k processing elements to recognize n input symbols, has time complexity O( n 3 k 2 ) ; if k = n, the time complexity will be O( n). If there are several such modules, we can perform parallel recognition of general context-free languages. Since these algorithms essentially speed-up the dynamic programming procedure by using highly pipelining and parallelism of VLSI architecture, the proposed algorithms may also be useful for other problems solvable by dynamic programming.

A Universal Syntax-Directed Top-Down Analyzer

An algorithm is described that will recognize, and fully analyze, strings of unbounded length, using the rewriting rules of any context-free grammar. It uses a finite random access store, three pushdown tapes, and a counter. It imposes no restrictions on the grammar defined by the rewriting rules, excepting only that it be a context-free phrase structure grammar. The analysis printed out is a linearized form of the structural description tree (or trees, in an ambiguous case) of the input string. A proof that the analyzer will always stop in a finite time is provided. The upper bound on the running time increases exponentially with input string length.