This thesis describes Tupleware, an implementation of a distributed tuple space which acts as a scalable and efficient cluster middleware for computationally intensive numerical and scientific applications. Tupleware is based on the Linda coordination language (Gelernter 1985), and incorporates additional techniques such as peer-to-peer communications and exploitation of data locality in order ...

The co-ordination language Linda has been used for parallel processing for many years. Linda uses a shared tuple space and a number of primitives to provide access to the tuple space and thereby enabling communication between processes executing concurrently. Linda provides asynchronous communication between processes, but synchronous access between the processes and the tuple spaces. The Bonit...

In this paper, we investigate the multiple attribute decisionmaking (MADM) problems with 2-tuple intuitionistic fuzzylinguistic information. Then, we utilize arithmetic and geometricoperations to develop some 2-tuple intuitionistic fuzzy linguisticaggregation operators. The prominent characteristic of theseproposed operators are studied. Then, we have utilized theseoperators to develop some app...

We consider setting up sleep scheduling in sensor networks. We formulate the problem as an instance of the fractional domatic partition problem and obtain a distributed approximation algorithm by applying linear programming approximation techniques. Our algorithm is an application of the Garg-Könemann (GK) scheme that requires solving an instance of the minimum weight dominating set (MWDS) prob...

We exploit the close relationship between circular arc graphs and interval graphs to design efficient approximation algorithms for NP-hard Optimization problems on circular arc graphs. The problems considered here are maximum domatic partition and on-line minimum vertex coloring. We present a heuristic for the domatic partition problem with a performance ratio of 4. For on-line coloring, we con...

Can AI help automate human-easy but computer-hard data preparation tasks that burden scientists, practitioners, and crowd workers? We answer this question by presenting RPT, a denoising autoencoder for tuple-to-X models (" X " could be tuple, token, label, JSON, so on). RPT is pre-trained tuple-to-tuple model corrupting the input tuple then learning to reconstruct original tuple. It adopts Tran...

This paper describes a generalisation of the binary N tuple technique originally described by Bledsoe and Browning (1). The binary N tuple technique has commonly been used for the classification (2) and pre-processing (3) of binary images. The extension to the method described here allows grey level images of objects to be classified using the N tuple method without first having to convert the ...

Let G be a connected graph. Let f be a proper k -coloring of G and Π = (R_1, R_2, . . . , R_k) bean ordered partition of V (G) into color classes. For any vertex v of G, define the color code c_Π(v) of v with respect to Π to be a k -tuple (d(v, R_1), d(v, R_2), . . . , d(v, R_k)), where d(v, R_i) is the min{d(v, x)|x ∈ R_i}. If distinct vertices have distinct color codes, then we call f a locat...