912 research outputs found
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Closing Keynote – The Liliana Rivera Garza Archive: The Afterlives of Femicide
No full textCristina Rivera Garza is an author, translator, and critic. She presents the first day's closing remarks on the Liliana Rivera Garza Archive.Teresa Lozano Long Institute of Latin American Studies; Latin America Initiative; Institute for Historical Studies; Not Even PastLatin American Studie
PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences
No full textNowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors located in different locations of the world. The data driven analysis of these large data sets by means of scalable machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness of PERCEIVE
Study of the applicability of an itemset-based portfolio planner in a multi-market context
No full textPlanning stock portfolios for long-term investments is a well-known financial problem. Many data mining and machine learning strategies have been proposed to automatically predict the set of uncorrelated stocks maximizing long-term portfolio returns. Among others, the use of scalable itemset-based strategies has recently been studied. Potentially, they can analyze large sets of historical prices corresponding to thousands of stocks in the worldwide market indexes. However, the current studies are still limited to single markets. This paper investigates the applicability of itemset-based strategies for planning stock portfolios in a multi-market context. Scaling the analyses towards multi-market scenarios poses a number of research questions, among which the choice of the diversification strategy, the influence of inter-market correlations among stock prices, and the profitability of multi-market strategies compared to single-market ones. This paper aims at answering to the aforesaid questions by considering a state-of-the-art itemset-based approach. The experimental results show that itemset-based strategies focus the generated portfolios on the outperforming markets. Furthermore, the performance of multi-market strategies with sector-based diversification is on average superior or comparable to single-market ones
Infrequent Weighted Itemset Mining Using Frequent Pattern Growth
No full textFrequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost function, discovering rare data correlations is more interesting than mining frequent ones. This paper tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI) mining problem. Two novel quality measures are proposed to drive the IWI mining process. Furthermore, two
algorithms that perform IWI and Minimal IWI mining efficiently, driven by the proposed measures, are presented. Experimental results show efficiency and effectiveness of the proposed approach
ViGEO: an Assessment of Vision GNNs in Earth Observation
No full textSatellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks
Majority Classification by Means of Association Rules
No full textAssociative classification is a well-known technique for structured data classification. Most previous work on associative classification based the assignment of the class label on a single classification rule. In this work we propose the assignment of the class label based on simple majority voting among a group of rules matching the test case. We propose a new algorithm, L3M, which is based on previously proposed algorithm L3. L3 performed a reduced amount of pruning, coupled with a two step classification process. L3M combines this approach with the use of multiple rules for data classification. The use of multiple rules, both during database coverage and classification, yields an improved accuracy
QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1
No full textEarthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to
estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor
the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary
coverage). Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional
stations. Using social media images in crisis management has proven effective in various situations. However,
they are still limited by the possibility of using communication infrastructures in case of an earthquake and by
the presence of people in the area. Moreover, social media images and messages cannot be used to estimate the
actual severity of earthquakes and their characteristics effectively. The employment of satellites to monitor changes
around the globe grants the possibility of exploiting instrumentation that is not limited by the visible spectrum, the
presence of land infrastructures, and people in the affected areas. In this work, we propose a new dataset composed
of images taken from Sentinel-1 and a new series of tasks to help monitor earthquakes from a new detailed view.
Coupled with the data, we provide a series of traditional machine learning and deep learning models as baselines to
assess the effectiveness of ML-based models in earthquake analysis
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