Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of $0.84$, a considerable improvement when compared to the Sen2Cor corresponding performance of $0.59$. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.

Cloud computing provides a computing platform for the users to meet their demands in an efficient way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines are utilized to satisfy the user needs and are placed on physical machines of the cloud for effective usage of hardware resources and electricity. Optimizing the number of physical machines used helps in cutting down the power consumption by substantial amount. An optimal technique is to map virtual machines to physical machines such that the number of required physical machines is minimized. The Virtual Machine Placement problem with the target of minimizing the total energy consumption by running physical machines is an indication of increasing resource utilization and reducing cost of a data center. Virtual Machine Migration is another approach to minimize the total energy consumption and to increase resource utilization. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. Migration support can balance the utilization of multi-dimensional resources, reduce the number of running physical machines and thus lower the energy consumption. In this paper, we present a platform to test different Virtual Machine Placement and Virtual Machine Migration algorithms on real time scenarios (Cloud) and enable them to conduct different experiments. For this purpose, OpenStack private cloud is suggested as private cloud environment and OpenStack Neat is used to provide dynamic Virtual Machine Migration. Here, we have integrated OpenStack Neat with OpenStack Cloud and shown the experiment of dynamic Virtual Machine Migration in real time.

Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents, objects, places, and time periods to be identified and represented. The extracted information can be represented by specialized ontologies, supporting knowledge-based reasoning and inference processes. In this work, we will describe, in detail, our proposal for event extraction from Portuguese documents. The proposed approach is based on a pipeline of specialized natural language processing tools; namely, a part-of-speech tagger, a named entities recognizer, a dependency parser, semantic role labeling, and a knowledge extraction module. The architecture is language-independent, but its modules are language-dependent and can be built using adequate AI (i.e., rule-based or machine learning) methodologies. The developed system was evaluated with a corpus of Portuguese texts and the obtained results are presented and analysed. The current limitations and future work are discussed in detail.

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become a prime component of any problem-solving. Examples include building classifiers for applications such as image/speech recognition, traffic prediction, product recommendation, virtual personal assistant (VPA), online fraud detection and many more. The performance of these developed classifiers is highly dependent upon the training dataset, and subsequently, without human intervention or true labels, the evaluation over unseen observations remains unknown. Using a statistical distance researchers did try to assess the model’s goodness-of-fit and compared multiple independent models. Nonetheless, given a train-test split and different classifiers built over the training set, the question ‘is it possible to find a prediction error using the relation between training and test set?’ remains unsolved. In this article, we propose a generalized statistical distance-based method measuring the prediction uncertainty at a new query point. To be specific, we propose a Mahalanobis distance-based Evidence Function Model to measure the misclassification caused by K-Nearest Neighbours (KNN), Extra Trees (ET), and Convolutional Neural Network (CNN) models when classifying Sentinel-2 image into six scene classes (Water, Shadow, Cirrus, Cloud, Snow, Other). The performance of the proposed method was assessed over two different datasets: (i) the test set, with an overall mean prediction uncertainty detection of 62.99%, 29.80% and 31.51%, leading to a mean micro-F1 performance of 67.89%, 39.30%, and 38.29% for KNN, ET, and CNN, respectively; (ii) a water-body set, with prediction uncertainty detection of 22.27%, 42.08%, and 27.67%, leading to a micro-F1 performance of 34.70%, 58.96%, and 43.32%, respectively.

The paper discusses the intersection of Cultural Tourism and topics that emerged during the IMPACTOUR project's lifespan. It showcases innovative approaches to managing Cultural Tourism and emphasizes essential trends related to tools and data. The paper also introduces the IMPACTOUR methodology and tool, which enhances the crucial role of Cultural Tourism stakeholders and offers a forward-looking perspective.