Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

Pseudo-Booleanilainen optimisaatio käyttäen implisiittisiä osumisjoukkoja

There are many computationally difficult problems where the task is to find a solution with the lowest cost possible that fulfills a given set of constraints. Such problems are often NP-hard and are encountered in a variety of real-world problem domains, including planning and scheduling. NP-hard problems are often solved using a declarative approach by encoding the problem into a declarative constraint language and solving the encoding using a generic algorithm for that language. In this thesis we focus on pseudo-Boolean optimization (PBO), a special class of integer programs (IP) that only contain variables that admit the values 0 and 1. We propose a novel approach to PBO that is based on the implicit hitting set (IHS) paradigm, which uses two separate components. An IP solver is used to find an optimal solution under an incomplete set of constraints. A pseudo-Boolean satisfiability solver is used to either validate the feasibility of the solution or to extract more constraints to the integer program. The IHS-based PBO algorithm iteratively invokes the two algorithms until an optimal solution to a given PBO instance is found. In this thesis we lay out the IHS-based PBO solving approach in detail. We implement the algorithm as the PBO-IHS solver by making use of recent advances in reasoning techniques for pseudo-Boolean constraints. Through extensive empirical evaluation we show that our PBO-IHS solver outperforms other available specialized PBO solvers and has complementary performance compared to classical integer programming techniques

Застосування SAT/SMT розв'язників в задачах кібербезпеки

Метою даної кваліфікаційної роботи є аналіз можливостей SAT/SMT – розв’язників, пошук кола їх можливих застосунків для задач кібербезпеки, зокрема для аналізу захищеності корпоративних мереж та розробка відповідного алгоритму застосування. Об’єктом дослідження є процес забезпечення захищеності мережі. Предметом дослідження є SAT/SMT –розв’язники в задачах кібербезпеки. Результати роботи можуть бути використані на практиці аудиторськими компаніями, які займаються аналізом та налаштуваннями безпеки великих корпоративних мереж. Запропонований алгоритм надає доказове підкріплення несуперечливості та повноти запропонованої політики захисту, і може використовуватись в складі інструментарію для аналізу стану захищеності мережі.The object of research is the process of ensuring network security. The subject of the research is SAT / SMT solvers in cybersecurity problems. Research methods are the study of literature sources, available software solutions, conducting an experiment using the software. Audit companies engaged in the analysis and security settings of large corporate networks can use the results of the work in practice. The proposed algorithm provides evidence-based support for the consistency and completeness of the proposed security policy and can be used as part of the tools to analyze the security status of the network

Engineering Graph Clustering Algorithms

Networks in the sense of objects that are related to each other are ubiquitous. In many areas, groups of objects that are particularly densely connected, so called clusters, are semantically interesting. In this thesis, we investigate two different approaches to partition the vertices of a network into clusters. The first quantifies the goodness of a clustering according to the sparsity of the cuts induced by the clusters, whereas the second is based on the recently proposed measure surprise

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts