Most large public datasets containing cyclists for training detectors based on Deep Learning have annotations for bicycles and people, but not for cyclists. Even when it is not the case, the quality and quantity of the images are limited. To overcome these limitations, we propose the new OpenImages Cyclists dataset, built through the pre-selection of images from the OpenImages set and a new algorithm for semiautomatic generation of cyclist annotation aided by people and bicycle detectors. A cyclist detector trained with this dataset achieved identification rates up to 78% and 89% in two different sets of images obtained from security cameras at USP, Campus São Paulo - Capital.
The concept of Smart Cities has gained relevance, especially in the last decade, due to the availability of data associated with cities, e.g., car traffic, public transportation, crime data, etc. The purpose of using these data is to improve the services offered to the citizens. Most of these applications manipulate spatiotemporal data. These data are processed in a dataflow that starts with the collection, integration, and aggregation and ends with visualization. This way, specialized data services for smart city applications are most welcome. However, many of the existing data services in this context, are either specific to a particular application/domain or do not consider the entire data life cycle. In this article, we present Hurricane, a dataflow-oriented data service for smart city applications. Hurricane executes multiple dataflows to gather, pre-process, integrate, and public data. Hurricane was evaluated with an application in the area of public security and results reinforced the importance of this type of data service.
A wide range of applications has used semi-structured data. A characteristic of this type of data is its flexible structure, i.e., it does not rely on schema-based constraints to define its entities. Usually entities of a same kind (i.e, class) do not present the same attribute set. However, some data processing and management applications rely on a data schema to perform their tasks. In this context, the lack of structure is a challenge for these applications to use this data. In this paper, we propose CoFFee, an approach to class schema discovery. Given a set of heterogeneous entity schemata, found within a class, CoFFee provides a summarized set with core attributes. To this end, CoFFee applies a strategy combining attributes co-occurrence and frequency. It models a set of entity schemata as a graph and uses centrality metrics to capture the co-occurrence between attributes. We evaluated CoFFee using data from 12 classes extracted from DBpedia and e-Commerce datasets. We benchmarked it against two other state-of-the-art approaches. The results show that: i) CoFFee effectively provides a summarized schema, minimizing non-relevant attributes without compromising the data retrieval rate; and ii) CoFFee produces a summarized schema of good quality, outperforming the baselines by an average of 19% of F1 score.
Based on openness and transparency for good governance, unimpeded and verifiable access to legal and regulatory information is essential. With such access, we can monitor government actions to ensure that public financial resources are not improperly or inconsistently used. This facilitates, for example, the detection of unlawful behavior in public actions, such as bidding processes and auctions. However, different public agencies have their own criteria for standardizing the models and formats used to make information available, as exemplified in the varying styles observed in municipal, state, and union (federal) documents. In this context, we aim to minimize the effort to deal with public documents, notably official gazettes. For this, we propose a structure-oriented heuristic for extracting relevant excerpts from their texts. We then characterize these excerpts through morphosyntactic analysis and entity recognition. Subsequently, we semantically classify the extracted fragments into "sections of interest" (e.g., bids, laws, personnel, budget) using an active learning strategy to reduce the manual labeling effort. We also improve the classification process by incorporating transformers, stacking, and by combining different types of representations (e.g., frequentist, static, and contextual semantic embeddings). Furthermore, we exploit oversampling based on semi-supervised learning to deal with (labeled) data scarceness and skewness. Finally, we combine all these contributions in a real-time annotation tool with active learning support that achieves 100% accuracy in extraction and an overall accuracy of 85% in classification with very little labeling effort.
In recent years, vast volumes of data are constantly being made available on the Web, and they have been increasingly used as decision support in different contexts. However, for these decisions to be more assertive and reliable, it is necessary to ensure data quality. Although there are several definitions for this area, it is a consensus that data quality is always associated with a specific context. This work aims to analyze data quality in a data warehouse with governmental information of the Brazilian state of Minas Gerais. We first present a brief comparison of eight open-source data quality tools and then choose the Great Expectations tool for analyzing such data in two real applications: public bids and public expenditure. Our analyses show that the chosen tool has relevant characteristics to generate good data quality indicators to reveal data quality issues that may directly impact the construction of final applications using such data.
