Scheduling linear network for space and time efficiency

2011 ◽  
Author(s):  
Arun K. Somani ◽  
Daniel Congreve
Keyword(s):  
Linear Network ◽  
Time Efficiency ◽  
Space And Time

scholarly journals Revisiting Multivariate Ring Learning with Errors and Its Applications on Lattice-Based Cryptography

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 858
Author(s):  
Alberto Pedrouzo-Ulloa ◽  
Juan Ramón Troncoso-Pastoriza ◽  
Nicolas Gama ◽  
Mariya Georgieva ◽  
Fernando Pérez-González
Keyword(s):  
Cyclotomic Polynomials ◽  
Time Efficiency ◽  
Space And Time ◽  
Low Degree ◽  
Learning With Errors ◽  
Lattice Based Cryptography ◽  
Low Degree Polynomials ◽  
Learning With Errors Problem ◽  
Recent Attack

The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, the recent attack presented by Bootland, Castryck and Vercauteren has some important consequences on the security of the multivariate RLWE problem with “non-coprime” cyclotomics; this attack transforms instances of m-RLWE with power-of-two cyclotomic polynomials of degree n=∏ini into a set of RLWE samples with dimension maxi{ni}. This is especially devastating for low-degree cyclotomics (e.g., Φ4(x)=1+x2). In this work, we revisit the security of multivariate RLWE and propose new alternative instantiations of the problem that avoid the attack while still preserving the advantages of the multivariate structure, especially when using low-degree polynomials. Additionally, we show how to parameterize these instances in a secure and practical way, therefore enabling constructions and strategies based on m-RLWE that bring notable space and time efficiency improvements over current RLWE-based constructions.

Space and time efficiency of the forest-of-quadtrees representation

1985 ◽  
Vol 3 (2) ◽  
pp. 63-70 ◽  
Author(s):  
Nancy K Gautier ◽  
S Sitharama Iyengar ◽  
Narinder B Lakhani ◽  
M Manohar
Keyword(s):  
Time Efficiency ◽  
Space And Time

Legal document recommendation system: a dictionary based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti P. Rana
Keyword(s):  
Recommendation System ◽  
Superior Performance ◽  
Time Efficiency ◽  
Content Type ◽  
Domain Specific ◽  
Space And Time ◽  
Enormous Amount ◽  
Corpus Size ◽  
Legal Document ◽  
Time To Learn

Purpose In the Indian judicial system, the court considers interpretations of similar previous judgments for the present case. An essential requirement of legal practitioners is to determine the most relevant judgments from an enormous amount of judgments for preparing supportive, beneficial and favorable arguments against the opponent. It urges a strong demand to develop a Legal Document Recommendation System (LDRS) to automate the process. In existing works, traditionally preprocessed judgment corpus is processed by Doc2Vec to learn semantically rich judgment embedding space (i.e. vector space). Here, vectors of semantically relevant judgments are in close proximity, as Doc2Vec can effectively capture semantic meanings. The enormous amount of judgments produces a huge noisy corpus and vocabulary which possesses a significant challenge: traditional preprocessing cannot fully eliminate noisy data from the corpus and due to this, the Doc2Vec demands huge memory and time to learn the judgment embedding. It also adversely affects the recommendation performance in terms of correctness. This paper aims to develop an effective and efficient LDRS to support civilians and the legal fraternity. Design/methodology/approach To overcome previously mentioned challenges, this research proposes the LDRS that uses the proposed Generalized English and Indian Legal Dictionary (GEILD) which keeps the corpus of relevant dictionary words only and discards noisy elements. Accordingly, the proposed LDRS significantly reduces the corpus size, which can potentially improve the space and time efficiency of Doc2Vec. Findings The experimental results confirm that the proposed LDRS with GEILD yield superior performance in terms of accuracy, F1-Score, MCC-Score, with significant improvement in the space and time efficiency. Originality/value The proposed LDRS uses the customized domain-specific preprocessing and novel legal dictionary (i.e. GEILD) to precisely recommend the relevant judgments. The proposed LDRS can be incorporated with online legal search repositories/engines to enrich their functionality.

Languages in Space and Time

2020 ◽  
Author(s):  
Marco Patriarca ◽  
Els Heinsalu ◽  
Jean Leó Leonard
Keyword(s):  
Space And Time

On Space and Time

2008 ◽  
Author(s):  
Alain Connes ◽  
Michael Heller ◽  
Roger Penrose ◽  
John Polkinghorne ◽  
Andrew Taylor
Keyword(s):  
Space And Time

Does the EEG Reflect Space- and Time-Coded Cerebral Functions?

1979 ◽  
Vol 24 (10) ◽  
pp. 824-824 ◽  
Author(s):  
DONALD B. LINDSLEY
Keyword(s):  
Space And Time
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