The issue of sustainability in education has never been more important for the future of our environment, and strategies to develop the skills needed by younger generations to meet this significant global challenge should be developed across all curricula. There is much focus on the topic of sustainability in business, finance, climate, health, water and education; however, there are some challenges when sustainability needs to be integrated into engineering or fundamental study programs (SPs). In the latter, sustainability is more often emphasized and implemented through its general principles or separate modules in social sciences and project activities. There are a number of questions and challenges in how to highlight sustainability aspects and evaluation metrics due to the specifics of the engineering study field. For evaluating the sustainability level in engineering studies, a hierarchical methodology employing the SAMR (Substitution, Augmentation, Modification, Redefinition) model is proposed, taking a technological university in Lithuania as the case study. As a more concrete example, the first and second cycle SPs titled ‘Artificial Intelligence’ are described and analyzed in all relevant perspectives of sustainability. The study proposes five tangible criteria that must be emphasized in the learning process in order to ensure the development of sustainability goals in IT/AI study programs.
In this paper the selection of the right Cloud in Intercloud of computing services for fulfilment of the user task is analyzed. The main problem approached is how to select the right Cloud in Intercloud for the user task when there are no available or relevant resources in the private and public Cloud. In this case the Cloud, which gives the calculation results to the user in the shortest time including waiting time in queue, should be selected. For selection of the right Cloud the QoGS method has been chosen. The principle schema of QoGS method application in Intercloud has been presented. However, QoGS method works properly if the right set of weighted coefficients (SoWC) is selected. Existing methods are not suitable for selection of weighted coefficients because they require sending a lot of test tasks to Intercloud. Therefore, a new algorithm for selection of the best SoWC is presented. Results of the experiment show that it allows minimizing the workload of Intercloud by test tasks significantly.
Vehicle detection and classification is an important part of an intelligent transportation surveillance system. Although car detection is a trivial task for deep learning models, studies have shown that when vehicles are visible from different angles, more research is relevant for brand classification. Furthermore, each year, more than 30 new car models are released to the United States market alone, implying that the model needs to be updated with new classes, and the task becomes more complex over time. As a result, a transfer learning approach has been investigated that allows the retraining of a model with a small amount of data. This study proposes an efficient solution to develop an updatable local vehicle brand monitoring system. The proposed framework includes the dataset preparation, object detection, and a view-independent make classification model that has been tested using two efficient deep learning architectures, EfficientNetV2 and MobileNetV2. The model was trained on the dominant car brands in Lithuania and achieved 81.39 % accuracy in classifying 19 classes, using 400 to 500 images per class.
Global digitization trends and the application of high technology in the garment market are still too slow to integrate, despite the increasing demand for automated solutions. The main challenge is related to the extraction of garment information-general clothing descriptions and automatic dimensional extraction. In this paper, we propose the garment measurement solution based on image processing technologies, which is divided into two phases, garment segmentation and key points extraction. UNet as a backbone network has been used for mask retrieval. Separate algorithms have been developed to identify both general and specific garment key points from which the dimensions of the garment can be calculated by determining the distances between them. Using this approach, we have resulted in an average 1.27 cm measurement error for the prediction of the basic measurements of blazers, 0.747 cm for dresses and 1.012 cm for skirts.
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