In this thesis, a content recommendation system has been developed. The system makes recommendations based on the preferences of the users on some aspects of the content and also preferences of similar users. The preferences of a user are extracted from the choices of that user made in the past. Similarities between users are defined by the similarities of their preferences. Such a system requires both qualified content and user information. The proposed system uses semantic user and content profiles to more effectively define the relationships between the two and make better inferences. An ontology is defined using the existing domain ontologies and the semi-structured data on the web. The system is implemented mainly for the movie domain in which well-defined ontologies and user information are easier to access.

Feature models are used for variability management in software product lines. Instead of developing a single feature model, merging small models can be an effective solution to obtain a unified view. Since each stakeholder views the product family from a different perspective, conflicts may occur during merging. In this research, merging of feature models arising from different viewpoints is considered. A normative procedure is proposed to merge feature models by applying local rules. This procedure can merge feature models with cross-tree relationships between sibling features. Application of the local rules is demonstrated with examples.

Real-world information containing subjective opinions and judgments has emerged the need to represent complex and imprecise data in databases. Additionally, the challenge of transferring information between databases whose data storage methods are not compatible has been an important research topic. Extensible Markup Language (XML) has the potential to meet these challenges since it has the ability to represent complex and imprecise data. In this thesis, an XML based fuzzy data representation and querying system is designed and implemented. The resulting system enables fuzzy querying on XML documents by using XQuery, a language used for querying XML documents. In the system, complex and imprecise data are represented using XML combined with the fuzzy representation. In addition to fuzzy querying, the system enables restructuring of XML Schemas by merging of elements of the XML documents. By using this feature of the system, one can generate a new XML Schema and new XML documents from the existing documents according to this new XML Schema. XML data used in the system are retrieved from Internet by Web Services, which can make use of XML&rsquo / s capabilities to transfer data and, XML documents are stored in a native XML database management system.

The goal of this thesis study is to develop a simulator toolkit for Motorola&rsquo / s 8-bit microcontroller MC6811. The toolkit contains a cross-assembler to obtain object code from the source code and a simulator to run the object code. Written document of this thesis study describes the properties of the MC6811 microcontroller and its assembly language. In addition, the document describes the cross-assembler and simulator parts of the toolkit with details. In the cross-assembler part of the toolkit, parsing of the source Code and processing of the parsed information is studied. The simulator part studies the execution of the object code generated by the crossassembler. The execution of each instruction and main functions of the microcontroller can be observed from a Graphical User Interface (GUI). The Central Processing Unit (CPU), the busses, ports and interrupts of the microcontroller are included into the GUI. C++ programming language is used to develop and to implement the toolkit.

There is a dilemma between security and usability, which are two fundamentally conflicting issues. From the usability perspective, authentication protocols should be easy to use and passwords generated from these protocols should be easy to remember. From the security perspective, passwords should be hard to guess and should not be written down or stored in a plain text. Instead of using text based passwords, graphical passwords have been proposed to increase both memorability and security. Biederman (1972) and Biederman, Glass, &amp / Stacy (1973) reported that the objects in a coherent image were recognized and identified more efficiently and quickly than the objects in a jumbled image in which the jumbled image was created by dividing the coherent image into sections and changing the position of the sections without rotating them. The study was designed to experimentally examine the differences in usability and security of the graphical password scheme by manipulating the coherence of the displayed image. Sixty-three volunteers participated in the main experiment. The participants were divided into groups according to the type of image they were presented in the password creation (either coherent-image or jumbled-image) task. Each participant created a graphical password and three days after the first session (i.e., second session) s/he tried to remember it in order to authenticate to the system. The results revealed that in the proposed graphical password scheme, using coherent image has more advantages over jumbled image in terms of usability and security.

With new paradigms in software engineering such as Software Product Lines, scope of reuse is enlarged from implementation upto design, requirements, test-cases, etc. In this thesis an ontology-based approach is proposed as a solution to systematic requirement reuse problem in software product lines, and the approach is supported with a reuse automation tool. A case study is performed on the projects of an industrial software product line using hereby proposed solution and then based on the evaluated metrics it&rsquo / s reported that the content of requirements specifications documents can be prepared upto 80% by derivation of reusable requirements.

Path planning is a crucial issue for virtual environments where autonomous agents try to navigate from a specific location to a desired one. There are several algorithms developed for path planning, but several domain requirements make engineering of these algorithms difficult. In complex environments, considering single objective for searching and finding optimal or sub-optimal paths becomes insufficient. Thus, multi objective cases are distinguished and more complicated algorithms to be employed is required. It can be seen that more realistic and robust results can be obtained with these algorithms because they expand solution perspective into more than one criteria. Today, they are used in various games and simulation applications. On the other hand, most of these algorithms are off-line and delimitate interactive behaviours and dynamics of real world into a stationary virtuality. This situation reduces the solution quality and boundaries. Hence, the necessity of solutions where multi objectivity is considered in a dynamic environment is obvious. With this motivation, in this work, a novel multi objective incremental algorithm, MOD* Lite, is proposed. It is based on a known complete incremental search algorithm, D* Lite. Solution quality and execution time requirements of MOD* Lite are compared with existing complete multi objective off-line search algorithm, MOA*, and better results are obtained.

Improvements in technology result in evolution of smart devices. One of such smart devices is wireless sensor nodes, which consist of a sensing board, a battery supply and a wireless antenna to transfer data. We can collect information from the environment by deploying thousands of these tiny smart devices. These devices can also be used to monitor natural habitats or used in giant machine parts for performance evolution. Energy efficient operation is an important issue for wireless sensor network design and clustering is one of the most widely used approaches for energy efficiency. This thesis study aims to analyze the performance of clustering algorithms for wireless sensor networks. We proposed five clustering algorithms and perform experiments by using real sensor hardware over different topologies to investigate energy efficiency of the clustering algorithms.

Software project managers usually use benchmarking repositories to estimate effort, cost, and duration of the software development which will be used to appropriately plan, monitor and control the project activities. In addition, precision of benchmarking repositories is a critical factor in software effort estimation process which plays subsequently a critical role in the success of the software development project. In order to construct such a precise benchmarking data repository, it is important to have defined benchmarking data attributes and data characteristics and to have collected project data accordingly. On the other hand, studies show that data characteristics of benchmark data sets have impact on generalizing the studies which are based on using these datasets. Quality of data repository is not only depended on quality of collected data, but also it is related to how these data are collected. In this thesis, a benchmarking methodology is proposed for organizations to collect benchmarking data for effort estimation purposes. This methodology consists of three main components: benchmarking measures, benchmarking data collection processes, and benchmarking data collection tool. In this approach results of previous studies from the literature were used too. In order to verify and validate the methodology project data were collected in two middle size software organizations and one small size organization by using automated benchmarking data collection tool. Also, effort estimation models were constructed and evaluated for these projects data and impact of different characteristics of the projects was inspected in effort estimation models.

Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups / people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behaviors of individuals in a crowd (object based) and using this knowledge to make deductions regarding the crowd behavior and analyzing the crowd as a whole (holistic based). In this work, a holistic approach is used to develop a real-time abnormality detection in crowds using scale invariant feature transform (SIFT) based features and unsupervised machine learning techniques.