This article presents an overview of integrability of procedural modeling techniques needed to create a complete virtual city with streets, roads, building lots, exteriors and interiors with arranged furniture. Techniques are distributed into four hierarchies: urban plan, buildings, interior and furniture. Each technique is analyzed from the aspect of control of space definition, style uniformity, automatic interaction with other hierarchies and ability to procedurally generate a result around existing content. Each paper presented in this survey contributes either as a new control feature that has a potential of integrating with other techniques of higher, lower or same level of hierarchy, or as a new important part for creating a complete procedural city from highest to lowest level of hierarchy. The paper is concluded with a discussion of strong links between each area in the chain and important challenges in procedural generation of a complete city.

In this work we will show a novel method for improving the performance of content-based image retrieval using a deep neural network. The main focus of the method is training the distance function using the deep neural network and transfer learning. An existing pretrained network for image classification is used as a basis. One of the method's benefits is the fact that the existing network is not retrained and features for the distance function are the neural network's layers which were trained and stored before. The method is tested with the publicly available VGG19 deep neural network. Obtained results are comparable or in some cases better than the state of the art methods with a similar approach.

In software engineering courses with many students, it is often impossible for a professor to give a detailed review of the software project for each student. One solution is to give students a task to make reviews for a few projects. One review consists of a comment on a project and a score. The score represents an ordinal number of the project when student sorts all projects in order by quality. The comment can be used as feedback to their colleagues and the score can be used in the final grade prediction. Double-blind peer review is used to prevent favoritism and unfairness. In the paper methods for random projects distribution, reviews gathering and analysis are presented. Methods have been used on over 100 projects and data gathered from them show moderate to high correlation with final grades. Project rank and project points have correlation factors from r=-0.6 to r=-0.66 and significance p<0.001. Projects have been independently graded with automatic tests.

Multi-, many-core, hybrid processors and parallel programming languages are slowly becoming pervasive in main- stream computing. It is expected that they will affect a large spectrum of systems, from embedded and general-purpose, to high-end computing systems. This architectural change has already challenged programmers to efficiently write an application code that can scale over many cores to utilize its computational power. Moreover, many heterogeneous architectures exist today, hence there was an emergent need for a uniform interface to these architectures. Recently, Khronos Group defined the Open Computing Language (OpenCL) for abstracting the underlying hardware, which enables software developers to write a portable code across different shared-memory architectures. In this paper, we introduce a new parallel implementation of one of the fastest image segmentation algorithms known as Simple Linear Iterative Clustering based on OpenCL. We evaluate the effectiveness of this implementation using only multi-core GPCPU. Our implementation is fully compatible with sequential implementation. When the algorithm is executed sequentially it utilizes only 25% of total computational power of a GPCPU for any image resolution, while its modified algorithm is able to utilize close to 100% for high resolution images. The resulting algorithm is up to 5×faster than its sequential counterpart.