Abstract: Air-gap is an efficient technique for the improving of computer security. Proposed technique uses backlight modulation of monitor screen for data transmission from infected computer. The optimization algorithm for the segmentation of video stream is proposed for the improving of data transmission robustness. This algorithm is tested using Mote Carlo approach with full frame analysis for different values of standard deviations of additive Gaussian noise. Achieved results show improvements for proposed selective image processing for low values of standard deviation about ten times.

Abstract: The Dial-a-Ride Problem (DARP) consists in serving a set of customers who specify their pickup and drop-off locations using a fleet of vehicles. The aim of DARP is designing vehicle routes satisfying requests of customers and minimizing the total traveled distance. In this paper, we consider a real case of dynamic DARP service operated by Padam which offers a high quality transportation service in which customers ask for a service either in advance or in real time and get an immediate answer about whether their requests are accepted or rejected. A fleet of fixed number of vehicles is available during a working period of time to provide a transportation service. The goal is to maximize the number of accepted requests during the service. In this paper, we propose an original and novel online Reinsertion Algorithm based on destroy/repair operators to reinsert requests rejected by the online algorithm used by Padam. When the online algorithm fails to insert a new customer, the proposed algorithm intensively exploits the neighborhood of the current solution using destroy/repair operators to attempt to find a new solution, allowing the insertion of the new client while respecting the constraints of the problem. The proposed algorithm was implemented in the opti- mization engine of Padam and extensively tested on real hard instances up to 1011 requests and 14 vehicles. The results show that our method succeeds in improving the number of accepted requests while keeping similar transportation costs on almost all instances, despite the hardness of the real instances. In half of the cases, reduction of the number of vehicles is attained, which is a huge benefit for the company.

Abstract: A skeleton representation of geometrical objects is widely used in computer graphics, computer vision, image processing, and pattern recognition. Therefore, efficient algorithms for computing planar skeletons are of high relevance. In this paper, we focus on the algorithm for computing the Voronoi skeleton of a planar object represented by a set of polygons. The complexity of the considered Voronoi skeletonization algorithm is O(N log N), where N is the total number of polygon’s vertices. In order to improve the performance of the skeletonization algorithm, we proposed theoretically justified shape optimization heuristics basing on polygon simplification algorithms. We evaluated the efficiency of such heuristics using polygons extracted from MPEG 7 CE-Shape-1 dataset and measured the execution time of the skeletonization algorithm, computational overheads related to the introduced heuristics and also the influence of the heuristic onto the accuracy of the resulting skeleton. As a result, we established the criteria allowing us to choose the optimal heuristics for Voronoi skeleton construction algorithm depending on the critical system’s requirements.

Abstract: Demand for energy is ever growing. Accurate prediction of energy demand of large buildings becomes essential for property management to operate these facilitates more efficiently and greener. Various temporal modelling provides reliable yet straightforward paradigm for short term building energy prediction. However newly constructed buildings, newly renovated buildings, or buildings that have energy monitoring systems newly installed do not have sufficient data to build energy demand prediction models. In contrast, established buildings often have vast amounts of data collected. The model learned from these data can be useful if transferred to buildings with little or no data. Two tree-based machine learning algorithms were introduced in this study on transfer learning. Datasets from two leisure centers in Melbourne were used. The results show that transfer learning is a promising technique in predicting accurately under a new scenario as it can achieve similar or even better performance compared to learning on a full dataset.