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The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user's query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The growth of social media worldwide has brought social benefits and challenges. One problem we highlight is the proliferation of hate speech on social media. We propose a novel method for detecting hate speech in texts using Cross-Lingual Learning. Our approach uses transfer learning from Pre-Trained Language Models (PTLM) with large corpora available to solve problems in languages with fewer resources for the specific task. The proposed methodology comprises four stages: corpora acquisition, the PTLM definition, training strategies, and evaluation. We carried out experiments using Pre-Trained Language Models in English, Italian, and Portuguese (BERT and XLM-R) to verify which best suited the proposed method. We used corpora in English (WH) and Italian (Evalita 2018) as the source language and the OffComBr-2 corpus in Portuguese (the target language). The results of the experiments showed that the proposed methodology is promising: for the OffComBr-2 corpus, the best state-of-the-art result was obtained (F1-measure = 92%). • The development of a new methodology for hate speech detection. • Portuguese hate speech detection using Cross-Lingual Learning. • Up to 20% performance improvement over other models using the OffComBr-2 corpus. [ABSTRACT FROM AUTHOR]

Thanks to the built-in GPS device embedded in almost all smartphones, the facility of tracking users’ positions fostered new research opportunities. Among these opportunities, of particular interest in this work is the field of route and destination prediction. Suggesting a user to take a deviation to avoid a congested route is among the potential benefits of our research. Many of the approaches available in the literature consolidate the Markov model as suitable to prediction. Moreover, the Prediction by Partial Matching (PPM) compression technique has presented encouraging results for predicting route and destination. Thus, this paper proposes a novel predictor that combines Markov model with PPM technique, extracting the better of these two approaches. Our user-personalized predictor is able to predict the route and destination automatically in a real-time manner, including places never visited by the user. We evaluated our model with real world data collected from 21 users, obtaining a precision rate between 63% and 82%. [ABSTRACT FROM AUTHOR]

Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%. [ABSTRACT FROM AUTHOR]

This article proposes a semi-automatic technique for the annotation of people in photographs. The technique uses context and content information and is based on a weighted sum of estimators, which results in a list of the person’s contacts that are more likely to be present in a photograph. Machine learning methods, such as multivariable linear regression and slope function, are adopted to filter and weight the estimators and eigenfaces for face recognition. The article also describes the results of experiments that were performed with a collection of 4050 photographs with 365 different people, which indicate that the proposed technique outperforms techniques that adopt only context or only content using as a performance metric the H-Hit rate of correct annotations. [ABSTRACT FROM AUTHOR]

In recent years, spatial data infrastructures (SDIs) have gained great popularity as a solution to facilitate interoperable access to geospatial data offered by different agencies. In order to enhance the data retrieval process, current infrastructures usually offer a catalog service. Nevertheless, such catalog services still have important limitations that make it difficult for users to find the geospatial data that they are interested in. Some current catalog drawbacks include the use of a single record to describe all the feature types offered by a service, the lack of formal means to describe the semantics of the underlying data, and the lack of an effective ranking metric to organize the results retrieved from a query. Aiming to overcome these limitations, this article proposes SESDI (Semantically-Enabled Spatial Data Infrastructures), which is framework that reuses techniques of classic information retrieval to improve geographic data retrieval in a SDI. Moreover, the framework proposes several ranking metrics to solve spatial, semantic, temporal and multidimensional queries. [ABSTRACT FROM AUTHOR]

Nowadays, Spatial Data Infrastructures (SDIs) play an important role in government agencies, at different levels: global, national, and local. They aim to improve the management and sharing of geospatial data. Nonetheless, these SDIs have been developed as information islands, in which a user's query is compared to metadata described only in their own catalog services. The lack of interaction among SDIs limits the potential of these infrastructures in providing geospatial data to a larger audience. This article presents a distributed architecture, based on a federation of SDIs which interact among themselves, using query propagation. This propagation facilitates data discovery and sharing. We also describe a distributed query processing service used to enable the resource discovery in distributed infrastructures. [ABSTRACT FROM AUTHOR]

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data. [ABSTRACT FROM AUTHOR]

Abstract: The use of High Strength Steel (HSS) S690 in highway bridge decks is not yet widespread. A research program investigated the optimal use of HSS in bridges. A comparative design was performed for a 21.5 m wide highway bridge, with a typical 80 m long inner span and a steel‐concrete composite twin plate girder deck, using the Eurocode standards. Three designs were produced: Design (A) with S355 NL steel grade, Design (B) with HSS S690 QL/QL1, and Design (C), also with HSS S690, but exploring different design options provided by welded joint treatments to improve fatigue and suggest possible enhancement of the present Eurocode rules. General guidelines for the design of highway bridge decks using HSS S690 are provided. [ABSTRACT FROM AUTHOR]

The advent of volunteered geographical information (VGI) has contributed to the growth of the amount of user-contributed spatial data around the world. Spatial data acquired from crowdsourcing environments may contain valuable information which can be useful in other research fields, such as Digital gazetteers, commonly used in Geographic Information Retrieval. Digital gazetteers have a powerful role in the geoparsing process. They need to be kept up-to-date and as comprehensive as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital documents. The detection of toponyms in digital texts such as social media posts is a bottom line for discovering useful spatially related information such as complaints regarding urban areas. In this context, this article proposes a method for gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied within a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter posts focusing solely on the messages aiming at evaluating the performance of the enriched system. The obtained results were encouraging and have provided a good basis for discussion. [ABSTRACT FROM AUTHOR]