This book offers an original and informative view of the development of fundamental concepts of computability theory. The treatment is put into historical context, emphasizing the motivation for ideas as well as their logical and formal development. The book starts with the foundational crisis of mathematics in the early twentieth century, and formalism, continues with classical computability theory, the quest for formalization, the Turing Machine, and early successes such as defining incomputable problems, and finishes with relative computability, the class of degrees of unsolvability and the arithmetical hierarchy.
COBISS.SI-ID: 1536557251
A modern computer system provides its support via system software that consists of applications such as an assembler, a linker, a loader and virtual machines. It is of prime importance to give students that are learning system-software concepts a solid base of knowledge without any unnecessary details. To make the subject easy to understand we designed a simulator for a hypothetical computer that is already used in several courses on system software. In the paper, we describe the simulator's behavior as well as its design and implementation. Additionally, we present three case studies of using a simulator in teaching and describe our experience of its use in a course on system software. From the experience of using the simulator in a pedagogical process we conclude that it decreases the time invested by the students to comprehend the topic, and at the same time it enables more in depth understanding.
COBISS.SI-ID: 10241620
We propose a holistic approach to the problem of reidentification in an environment of distributed smart cameras on a locally connected topology. We model the reidentificationprocess in a distributed camera network as a distributed multiclass classifier, composed of spatially distributed binary classifiers. We treat the problem of reidentification as an open world problem, and address novelty detection and forgetting. We propose new measures for performance analysis of reidentification systems, which address problems that appear specifically due to online learning, forgetting and operation in the open world mode. The proposed concept is illustrated and evaluated on a new many camera surveillance dataset (Dana36 dataset) and well known SAIVT SoftBio dataset. We provide full source code with the paper, enabling the readers to duplicate all our experiments and extend them with their own algorithms.
COBISS.SI-ID: 10896980
In the book "Parallel Scientific Computing", where we present the background and motivation for the development of solution methodologies for partial differential equations. The book is concentrated on the synergy between computer science and numerical analysis. It is written to provide a firm understanding of the described approaches to computer scientists, engineers or other experts who have to solve real problems. The meshless solution approach is described in more details, with a description of the required algorithms and methods that are needed for the design of efficient computer programs. Most of the details are demonstrated on the solutions of practical problems, from basic to more complicated ones. This book is a useful tool for readers interested in implementation of complex computer simulations in various application domains.
COBISS.SI-ID: 28468007
The paper tackles a real-world problem in the domain of biomedicine – prediction of inner knee temperatures during therapeutic cooling (cryotherapy) after anterior cruciate ligament (ACL) reconstructive surgery. A validated simulation model of the cryotherapeutic treatment is used to generate a substantial amount of diverse data from different simulation scenarios. We apply machine learning methods on the simulated data to construct a predictive model that provides a prediction for the inner temperature variable based on other system variables whose measurement is more feasible, i.e. skin temperatures. We investigate the predictive performance and time/memory efficiency of several predictive modeling methods: linear regression, regression trees, model trees, and ensembles of regression and model trees. The model trees perform the best with prediction error in the same range as the accuracy of the simulated data (0.1 °C). Furthermore, they satisfy the requirements for small memory size and real-time response.
COBISS.SI-ID: 28857127