Brazil

Dependency Parsers (DP) are parsers that analyze dependencies between words in a sentence. Currently, dependency parser evaluation is a problem whose solutions are not well defined in the scientific community. Although the DP intrinsic metrics are the foremost choice of evaluation, extrinsic evaluation enables a different evaluation based on a downstream. Different results of DP can vary according to the domain task. Thus, this work applies an Open Information Extraction (OIE) method in Portuguese to provide an extrinsic evaluation of a set of CONLL Dependency Parsers. Our results demonstrate that there is a difference in the evaluation of Dependency Parsers considering a particular task. 


BRACIS 2020

CoNLL Dependency Parser: Extrinsic Evaluation through the Open Information Extraction task

open information extraction

natural language processing

dependency parsing

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

technical paper

The lackadaisical quantum walk is a graph search algorithm for 2D grids whose vertices have a self-loop of weight l. Since the technique depends considerably on this l, research efforts have been estimating the optimal value for different scenarios, including 2D grids with multiple solutions. However, specifically
two previous works have used different stopping conditions for the simulations. Here, firstly, we show that these stopping conditions are not interchangeable. After doing such a pending investigation to define the stopping condition properly, we analyze the impacts of multiple solutions on the final results achieved by
the technique, which is the main contribution of this work. In doing so, we demonstrate that the success probability is inversely proportional to the density of vertices marked as solutions and directly proportional to the relative distance between solutions. These relations presented here are guaranteed only for high
values of the input parameters because, from different points of view, we show that a disturbed transition range exists in the small values.

Impacts of Multiple Solutions on the Lackadaisical Quantum Walk Search Algorithm

Standard features used for Credit Scoring includes mainly registration and financial data from customers. However, exploring new features is of great interest for financial companies, since slight improvements in the person score directly impact the company revenue. In this work, we categorize features from open credit scoring datasets and compare them with the features found in a real company dataset. The company dataset contains unusual feature groups such as historical, geolocation, web behavior, and demographic data. We performed bivariate tests using the Kolmogorov-Smirnov metric and features to assess the performance of the particular feature groups. We also generated a score of good payer by using AdaBoost, Multilayer Perceptron, and XGBoost algorithms. Then, we analyzed the results with different metrics and compared them with the real company results. Our main finding was that these features added a small improvement to current datasets. We also identified the most promising feature groups and noticed that the tuned XGBoost performed better than the company solution in three out of four deployed metrics.


Impact of Unusual Features in Credit Scoring Problem

The political campaigns in Brazilian elections are mostly financed by public money. Every candidate has to provide detailed accountability reports to the legal authorities, which must be analyzed in a short time frame in search of eventual fraud or suspicious transactions. In this work we have compiled a real data set from 2016 Brazilian elections for all city councils in the São Paulo state and used it to propose a framework of data segmentation analysis and validation. An exploratory data analysis is performed to determine the features distribution and to define the required feature pre-processing tasks. A clustering analysis using DBSCAN method is applied to a subset of the original data, focused on segmenting the spending data regarding contracts with car fuel providers and detecting potential outliers. Three clusters were identified and a ridge regression model was used to evaluate the most important features on cluster definition. One cluster was related to candidates that received zero votes and the remaining two discriminated suppliers if they had or not contracts almost exclusively related to candidate spending on car fuel. The hyperparameters from the clustering analysis were validated using a bootstrap method and a null hypothesis of data set structure randomness was rejected using a Monte Carlo approach.

Spending Segmentation and Outlier Detection in Brazilian Elections

Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit's structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit's graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains

Learning Probabilistic Sentential Decision Diagrams by Sampling

In recent years, bitcoin has become a very attractive investment in financial industry, which is not controlled by governments, but is based on trust between transfers under the technology of block chain. Hence, forecasting future bitcoin cryptocurrency values is a problem that has attracted the attention of many researchers in the field, while proving to be a very challenging problem. This work presents an experimental analysis using LSTM and GRUs for forecasting bitcoin values in a minute-granulated time for the entire next day. To this end we also present our methodology for conducting the experiments. The final goal is to create the core of a financial prediction tool around the RNNs. In our experiments, we achieved interesting results such as a SMAPE of 0.0002, a RMSE of US$ 3.844 and a rRMSE of 0.0028 in a day where bitcoin rates vary from US$ 13.2K and US$ 14.6K, surpassing the results of SMAPE found in the literature and proposed limit of SMAPE smaller than 0.007 for forecasts.

Short-term forecasting in bitcoin time series using LSTM and GRU RNNs

Echo State Networks (ESNs) are recurrent neural networks that map an input signal to a high-dimensional dynamical system, called reservoir, and possess adaptive output weights. The output weights are trained such that the ESN's output signal fits the desired target signal. Classical reservoirs are sparse and randomly connected networks. In this article, we investigate the effect of different network topologies on the performance of ESNs. Specifically, we use two types of networks to construct clustered reservoirs of ESN: the clustered Erdös–Rényi and the clustered Barabási-Albert network model. Moreover, we compare the performance of these clustered ESNs (CESNs) and classical ESNs with the random reservoir by employing them to two different tasks: frequency filtering and the reconstruction of chaotic signals. By using a clustered topology, one can achieve a significant increase in the ESN's performance.

Clustered Echo State Networks for Signal Observation and Frequency Filtering

In this work, the technical feasibility of working with audio transcriptions from Youtube is analyzed, as well as presenting a method that allows data acquisition, pre-processing, and post-processing to work with this type of data. A topic modeling approach with the latent dirichlet allocation algorithm is used. An approach is also presented to dynamically determine the ideal number of topics that make up a given corpus. In the experiments, a database of 250 audio transcriptions was used, obtaining a model with coherence in the range of 40%.

From audio to information: Learning topics from audiotranscripts

The Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019. In a few months, the disease got pandemic proportions, overloading health systems all around the world. Risk factors related to the progression and outcome of the disease are still unclear. Moreover, clinical aspects of patients can differ between societies, and other demographic elements may impact survival responses. A better characterisation of local manifestation of COVID-19 is crucial to a better general understanding of the disease, and thus to improve treatment decisions and health systems’ management. In this article, we performed an initial analysis of clinical factors related to admission in ICU or death of SARS-CoV-2 confirmed Brazilian patients, based on 1,138,690 medical records from the Brazilian government. To our knowledge, this study is the first to assess clinical risk factors for disease progression in Brazil. We provide a concise data set of medical registers related to COVID-19 in the whole Brazilian territory, and we describe the baseline comorbidities and symptoms observed in the data collection. Then, we assess the correlation between the manifestation of symptoms/comorbidities and the patients’ survival response through Kaplan-Meier survival estimates. The results here reported are mostly in accordance with findings reported in previous works.

Clinical risk factors of ICU & fatal COVID-19 cases in Brazil

Fake news (FN) have affected people's lives in unimaginable ways. The automatic classification of FN is a vital tool to prevent their dissemination and support fact-checking. Related work has shown that FN spread faster, deeper, and more broadly than the truth on social media. Besides, deep learning has produced state-of-the-art solutions in this field, mainly based on textual attributes. In this paper, we propose initial experiments to combine compact representations of the textual news properties generated using DistilBERT, with topological metrics extracted from the social propagation network. Using a dataset related to politics and five distinct classification algorithms, our results are encouraging. Regarding the textual attributes, we reached results comparable to state-of-the-art solutions using only the news title and contents, which is useful for FN early detection. The topological attributes were not as effective, but the promising results encourage the investigation of alternative architectures for their combination.

Combining compact news representations generated using DistilBERT and topological features to classify fake news

Twitter has been one of the main sources of information and discussion during the COVID-19 pandemics. This paper characterizes a set of more than 56 million tweets written in Portuguese and collected over a period of 70 days. Our analysis includes the volume of messages, text of tweets, location of tweets, the main elements of tweets (e.g. hashtags and URLs) and the user profiles, including gender. The analyses showed the most discussed topics in the period were quarantine, hydroxychloroquine, agglomeration and social distance, and that the discussions were centered in political issues (e.g., most common hashtags include “fechadocombolsonaro" and “forabolsonaro").

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