Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2017
Volume
Vol. 18
Number
No. 3
Description
Reviewed by: Tamas Kozsik, Bartosz Klin, Marcin Orchel, Szymon Bobek, Emir Huseyin Ozder, Ozcan Ozturk, Tomasz Arodz, Dariusz Krol, Jean-Charles Creput, Jaroslaw Kozlak, Carlos Cotta, Kevin Scannell, Tunde Adegbola
Journal Volume
Computer Science
Vol. 18 (2017)
Projects
Pages
Articles
Type-driven development of concurrent communicating systems
(Wydawnictwa AGH, 2017) Brady, Edwin
Modern software systems rely on communication, for example mobile applcations communicating with a central server, distributed systems coordinaing a telecommunications network, or concurrent systems handling events and processes in a desktop application. However, reasoning about concurrent prgrams is hard, since we must reason about each process and the order in which communication might happen between processes. In this paper, I describe a type-driven approach to implementing communicating concurrent programs, using the dependently typed programming language Idris. I show how the type system can be used to describe resource access protocols (such as controlling access to a file handle) and verify that programs correctly follow those prtools. Finally, I show how to use the type system to reason about the order of communication between concurrent processes, ensuring that each end of a communication channel follows a defined protocol.
Modeling and rendering of convective cumulus clouds for real-time graphics purposes
(Wydawnictwa AGH, 2017) Kobak, Paweł; Alda, Witold
The paper presents a simulation and rendering model of three dimensional covective cloud evolution. The model is physically based, however its purpose is graphical. The main stress is put on balancing two parts of a model: the atmsphere simulation with convective motion of air and water vapor combined with rendering of semi-transparent and light-scattering clouds, in order to achieve realistic animation in real-time. We examine and compare two algorithmic approaches based on CPU and GPU computations.
Multi-objective optimization of vehicle routing problem using evolutionary algorithm with memory
(Wydawnictwa AGH, 2017) Podlaski, Krzysztof; Wiatrowski, Grzegorz
The idea of a new evolutionary algorithm with memory aspect included is proposed to find multiobjective optimized solution of vehicle routing problem with time windows. This algorithm uses population of agents that individually search for optimal solutions. The agent memory incorporates the process of learning from the experience of each individual agent as well as from the experience of the population. This algorithm uses crossover operation to define agents evolution. In the paper we choose as a base the Best Cost Route Crossover (BCRC) operator. This operator is well suited for VPRTW problems. However it does not treat both of parent symmetrically what is not natural for general evolutionary processes. The part of the paper is devoted to find an extension of the BCRC operator in order to improve inheritance of chromosomes from both of parents. Thus, the proposed evolutionary algorithm is implemented with use of two crossover operators: BCRC and its extended-modified version. We analyze the results obtained from both versions applied to Solomon’s and Gehring & Homberger instances. We conclude that the proposed method with modified version of BCRC operator gives statistically better results than those obtained using original BCRC. It seems that evolutionary algorithm with memory and modification of Best Cost Route Crossover Operator lead to very promising results when compared to the ones presented in the literature.
Using Redis supported by NVRAM in HPC applications
(Wydawnictwa AGH, 2017) Malinowski, Artur
Nowadays, the efficiency of a storage systems is a bottleneck in many moern HPC clusters. High performance in traditional approach - processing using files - is often difficult to obtain because of model complexity and its read/write patterns. Alternative approach is applying a key-value database, which usually has low latency and scales well. On the other hand, many key-value stores suffer from limitation of memory capacity and vulnerability to serious faiures, which is caused by processing in RAM. Moreover, some research suggests, that scientific data models are not applicable to storage structures of key-value databases. In this paper, the author proposes resolving mentioned issues by replacing RAM with NVRAM. Practical example is based on Redis NoSQL. The article contains also a three domain specific APIs, that show the idea bhind transformation from HPC data model to Redis structures, as well as two micro-benchmarks results.
Restoring tone-marks in standard yorùbá electronic text: improved model
(Wydawnictwa AGH, 2017) Asahiah, Franklin Ọládiípò; Ọdéjọbí, Ọdétúnjí Àjàdí; Adagunodo, Emmanuel Rotimi
Diacritic Restoration is a necessity in the processing of languages with Latinbased scripts that utilizes letters outside the basic Latin alphabet used by English language. Yorùbá is one such languages, marking underdot (dot-below)on three characters and tone marks on all seven vowels and two syllabic nasals. The problem of restoring underdotted characters has been fairly addressed using character as linguistic units for restoration. However, the existing characterbased approaches and word-based approach has not been able to sufficiently address restoration of tone marks in Yorùbá. We address in this study tone marks restoration as a subset of diacritic restoration. We proposed using the syllable (derived from word) as the linguistic token for tone marks restoration. In our experimental setup, we used Yoruba text collected from various sources as data with total word count of 250,336 words. These words, on syllabification, yielded 464,274 syllables. The syllables were divided into training and testing data in different proportions ranging from 99% used for training and 1% used for testing to 70% used for training and 30% used for testing. The aim of evaluation different proportions was to determine how the ratio of training-to-test data affect the variations that may occur in the result. We applied Memory-based learning to train the models. We also set up a similar experiment using character token to be able to compare the performance. The result showed that using syllable was able to increase accuracy at word level to 96.23% and an average of almost 15% over that gotten from using character. We also found out that using 75% of data for training and the remaining 25% for testing gives the results with the least variation in a ten-fold cross validation test. Hybridizing this method that uses syllabless as processing linguistic units with other methods like lexicon lookup might likely lead to improvement over the current result.