Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2016
Volume
Vol. 17
Number
No. 2
Description
Reviewed by: Tomasz Arodz, Piotr Gawrysiak, Marisa Gil, Jozef Juhar, Anna Jordanous, Krzysztof Korcyl, Waclaw Kus, Agnieszka Landowska, Maria Llano, Pawel Matuszyk, Maciej Paszynski, Dana Petcu, Tony Shardlow, Georgi Stojanov, Bartosz Walter
Journal Volume
Computer Science
Vol. 17 (2016)
Projects
Pages
Articles
Hybrid computer system for the identification of metallic material models on the basis of laboratory experiments
(Wydawnictwa AGH, 2016) Rauch, Łukasz; Górecki, Grzegorz; Pietrzyk, Maciej
The identification of the proper parameters of material models plays a crucial role in the design of production technologies, especially in the case of modern materials with diversified properties under different boundary conditions. The procedure of identification is usually based on an optimization algorithm that uses sophisticated numerical simulations as a part of the goal function and compares the obtained results with experimental tests. Despite its reliability, such an approach is numerically inefficient. This paper presents the concept of how to replace the most numerically-demanding part of the identification procedure with metamodels, allowing us to maintain uniform result quality. The computer system, which allows us to manage input data, metamodels, and calculations, is proposed and described in detail in this paper. Finally, the proposed approach is validated on the basis of tests performed in the laboratory.
Source code reviews on mobile devices
(Wydawnictwa AGH, 2016) Frącz, Wojciech; Dajda, Jacek
This paper presents the results of an experiment-driven investigation on the efficiency of source code review practice performed on mobile devices. In particular, the conducted investigation tries to verify whether or not the small screens of mobile devices influence the speed and quality of the review process. Besides presenting the experiment itself and discussing the obtained results, this paper also describes the dedicated Android application for mobile code reviews that was implemented for research purposes.
Building sentiment lexicons based on recommending services for the Polish language
(Wydawnictwa AGH, 2016) Gliwa, Bogdan; Zygmunt, Anna; Dąbrowski, Michał
Sentiment analysis has become a prominent area of research in computer science. It has numerous practical applications, e.g., evaluating customer satisfaction, identifying product promoters. Many methods employed in this task require language resources such as sentiment lexicons, which are unavailable for the Polish language. Such lexicons contain words annotated with their emotional polarization, but the manual creation of sentiment lexicons is very tedious. Therefore, this paper addresses this issue and describes a new method of building sentiment lexicons automatically based on recommending services. Next, the built lexicons were used in the task of sentiment classification.
Dynamic Turing Machine - model and properties for runtime code changes
(Wydawnictwa AGH, 2016) Rudy, Jarosław
In this paper, a dynamic model of computation based on the Universal Turing Machine is proposed. This model is capable of applying runtime code modifications for 3-symbol deterministic Turing Machines at runtime and requires a decomposition of the simulated machine into parts called subtasks. The algorithm for performing runtime changes is considered, and the ability to apply runtime changes is studied through computer simulations. Theoretical properties of the proposed model, including computational power as well as time and space complexity, are studied and proven. Connections between the proposed model and Oracle Machines are discussed. Moreover, a possible method of implementation in real-life systems is proposed.
On the computational cost and complexity of stochastic inverse solvers
(Wydawnictwa AGH, 2016) Faliszewski, Piotr; Smołka, Maciej; Schaefer, Robert; Paszyński, Maciej
The goal of this paper is to provide a starting point for investigations into a mainly underdeveloped area of research regarding the computational cost analysis of complex stochastic strategies for solving parametric inverse problems. This area has two main components: solving global optimization problems and solving forward problems (to evaluate the misfit function that we try to minimize). For the first component, we pay particular attention to genetic algorithms with heuristics and to multi-deme algorithms that can be modeled as ergodic Markov chains. We recall a simple method for evaluating the first hitting time for the single-deme algorithm and we extend it to the case of HGS, a multi-deme hierarchic strategy. We focus on the case in which at least the demes in the leaves are well tuned. Finally, we also express the problems of finding local and global optima in terms of a classic complexity theory. We formulate the natural result that finding a local optimum of a function is an NP-complete task, and we argue that finding a global optimum is a much harder, DP-complete, task. Furthermore, we argue that finding all global optima is, possibly, even harder (#P-hard) task. Regarding the second component of solving parametric inverse problems (i.e., regarding the forward problem solvers), we discuss the computational cost of hp-adaptive Finite Element solvers and their rates of convergence with respect to the increasing number of degrees of freedom. The presented results provide a useful taxonomy of problems and methods of studying the computational cost and complexity of various strategies for solving inverse parametric problems. Yet, we stress that our goal was not to deliver detailed evaluations for particular algorithms applied to particular inverse problems, but rather to try to identify possible ways of obtaining such results.