Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given to performance and privacy issues, as the system includes social aspects such as location monitoring.

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the algorithm has to be adapted to this problem, including crossover and mutation operators and introducing quantum principles in particular procedures. The results have shown that the performance of our approach in terms of converging to the best solutions is satisfactory. Moreover, we have presented the results of the selected parameters of the approach on the quality of the obtained solutions.

The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A significant challenge in using deep learning methods is the ability to explain the decision-making process of the network. To ensure accurate results, the deep network being used must undergo extensive training to produce high-quality predictions. There are various network architectures that differ in their properties and number of parameters. Consequently, an intriguing question is how these different networks arrive at similar or distinct decisions based on the same set of prerequisites. Therefore, three widely used deep convolutional networks have been discussed, such as VGG16, ResNet50 and EfficientNetB7, which were used as backbone models. We have customized the output layer of these pre-trained models with a softmax layer. In addition, an additional network has been described that was used to assess the saliency areas obtained. For each of the above networks, many tests have been performed using key metrics, including statistical evaluation of the impact of class activation mapping (CAM) and gradient-weighted class activation mapping (Grad-CAM) on network performance on a publicly available dataset of brain tumour X-ray images.

This paper is devoted to the implementation of the method based on the theoretical properties of the quadratic assignment problem in the implementation of the nature-inspired algorithms. The equation describing the conditional expectation value for the QAP problem objective function was proposed and proved. The conditional expectation was used in the implementation of several types of pseudo-genetic operators. The results obtained with the use of new operators were compared with the results obtained with the use of standard pseudo-genetic operators for the QAP problem.

This paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.