Drazen Draskovic

Transition from traditional to LMS supported examining: A case study in computer engineering

Modern education is hard to imagine without the use of e‐learning tools, but still the transition from traditional “paper‐and‐pencil” examining to electronic exams is to some extent cautious. Although course administration and exam evaluation are much easier with learning management systems, there are concerns among teaching staff, that electronic tests simplify examination process compared to paper tests and classic examining. At the University of Belgrade, School of Electrical Engineering, e‐learning tools are used at several exams, mostly as a secondary tool to aid with laboratory exercises. In this paper, we show our experience with four courses from the computer engineering study program that are to various extent moved to Moodle LMS, and electronic examining. We mostly concentrate on certain aspects of transition from paper tests to electronic exams. We present 12 different transformations needed to conform to electronic examining and automated evaluation, and discuss benefits and drawbacks of such a transition.

A software agent for social networks using natural language processing techniques

Machine-learning techniques are widely used in the computer processing of natural language. Software agents are programs that use machine learning and natural language processing to communicate with users and to perform certain tasks or provide specific information. This paper provides an overview of basic software agents and describes the implementation of an intelligent software agent for social network Facebook Finally, the results of the research propose potential improvements of implemented system.

Application of Moodle platform in computer engineering courses

In the modern educational process e-learning tools have an increasing application. There are a number of systems that can be used for various purposes and at various levels of education. This paper presents experiences in using one such system as a platform for teaching in higher education. Moodle platform has been successfully used at several computer engineering courses at the School of Electrical Engineering, University of Belgrade. The paper explaines to what purpose the system was used and how the transition from traditional teaching was made. The perceived advantages and disadvantages of the use of such a system are given.

Educational software system for reasoning and decision making using Bayesian networks

Artificial intelligence represents important area in the field of Computer Science and Engineering. The paper describes Bayesian thinking and the software system for learning Bayesian networks which has been realized at the University of Belgrade - School of Electrical Engineering. This software has been developed as visual interactive educational system for students and will be used within undergraduate studies, within the course subject ‘Intelligent Systems.’ In this system it is possible to create arbitrary Bayesian network and demonstrate the process of reasoning and decision making, through detailed monitoring of the process. The main objectives of using the system are: to simplify the learning process, and make it efficient and easily integrated into the course.

Software system for expert systems learning

This paper describes a software system for expert systems learning realized at the School of Electrical Engineering at the University of Belgrade. This software has been developed as visual interactive educational system for students and is aimed to be used within undergraduate and graduate courses. The system guides students through examples and step-by-step tasks related to topics that represent a part of lectures and tutorials. The following topics are covered: breadth-first and depth-first strategies, hill-climbing method, best-first method, branch and bound methods, method A*, production systems, general problem solver, STRIPS planning system and undetermined environment operation systems - fuzzy logic, probability factor reasoning and other ways of expressing uncertainty, ID3 decision tree induction algorithm. Students may enter they own examples and tasks and thus obtain correct solutions. At every stage, it is possible to go backward or forward in the simulation. Student may also print the detailed how-to procedure of solving the task. The implemented system improves lecturers efficiency and enhances knowledge acquisition of innovative curricula.

A classification of mutational approaches for genetic search

Genetic algorithms for Internet Search were classified a lot in the open literature, but one specific aspect there off - the mutational approaches - was not. This paper represents an effort to shot light on the existing mutational approaches in the context of the genetic algorithms that they are a part of. Major contributions of this paper are: (a) An original classification, which opens some potentially fruitful research avenues; (b) A block diagram based representation of the four major classes of the newly introduced classification; (c) Uniform pseudo-code based presentation of selected algorithms, in a way that enables easy comparison; (d) Discussion of essential issues in a way that opens up new avenues for future research.

SAIL-Software system for learning AI algorithms

Artificial intelligence (AI) comprises a large spectrum of groups of algorithms: heuristic algorithms for search and planning, formal methods for representation of knowledge and reasoning, algorithms for machine learning and many more. Since these algorithms are complex, there is a need for a system which would enable their application both in everyday work and education processes. This paper describes a software system for learning AI algorithms called SAIL (Software System for AI Learning), which can be used both on computers and mobile devices. The paper gives examples of lab exercises and self‐study tasks that through graphic representation and detailed procedures help students master this area. Students can enter their examples into the system and obtain correct solutions for those examples. At any point when an example is simulated, a student can proceed to the next step or go back to the previous one, save the current simulation as a file, or print the detailed procedure as a task solution. SAIL helps lecturers go through the syllabus more efficiently and improve class material, while at the same time it helps students get a better grasp of implemented algorithms. SAIL can also benefit software engineers, who can select and simulate an adequate algorithm to solve a specific problem. The results of the SAIL system are verified within the AI introductory course at the School of Electrical Engineering University of Belgrade and they are presented in this paper.

Chapter One – A New Course on R&D Project Management in Computer Science and Engineering: Subjects Taught, Rationales Behind, and Lessons Learned

Abstract This chapter describes the essence of a course for senior level undergraduate students and for master students of computer science and engineering, and analyzes its effects. The course prepares students for their professional life after graduation, and especially, it prepares them for the challenges related to efforts to bring new paradigm-shifting ideas into the commercial world. This course was developed to complement a DataFlow course and to teach DataFlow researchers about issues of importance for promotion of their results with a commercial potential. Consequently, course examples and homework assignments were chosen to reflect issues of importance in the commercialization of the DataFlow concept. The course includes the following subjects (presented with DataFlow-related issues in mind): (a) Writing proposals for Research and Development in industry and academia, (b) Understanding the essence of the MBA/PhD degrees and preparing the GMAT/GRE analytical exam, (c) Understanding Capability Maturity Model Integration and learning how to write holistic strategic project plans, (d) Understanding Project Management and learning how to write detailed tactical project plans, (e) Writing business plans for venture capital or business angels, (f) Writing patent applications, (g) Writing survey papers for SCI (Science Citation Index) journals, (h) Writing research papers for SCI journals, (i) Making an Internet shop, (j) Making a MindGenomics campaign for the Internet shop, (k) DataMining from project history and project experiments, and (l) Preservation of project heritage and skills related to brand making. Each subject matter is covered by a homework assignment to help deepen the practical knowledge of the subject matter covered. In addition to the above described, which is accompanied with homework, the following four subjects are also covered and accompanied with in-class discussions (oriented to DataFlow research): (m) Inventivity, (n) Creativity, (o) Effectiveness, and (p) Efficiency. Consequently, the analysis part concentrates on the following issues: (a) Inventivity: How different majors react to the subject matters, (b) Creativity: How efficiently the initial knowledge gaps get bridged, (c) Effectiveness: How the experience of the teacher helps, and (d) Efficiency: How the previous experiences of students help.

Hybrid approaches to mutation in genetic search algorithms

Genetic algorithms for Internet search have been classified in a recent paper of the same authors, as indicated in the reference section of this paper. The proposed new classification was based on a survey of the open literature and recognizes four basic approaches. This paper analyses these four approaches, and discusses their potentials, especially in the domain of hybridization, i.e., in the domain of generation of new hybrid approaches that take the best of one or more of the existing approaches. Two different hybrid approaches are described and their performance potentials are discussed in a way that opens up new avenues for future research.