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Reviews are valuable sources of information to support the decision making process. Therefore, the task of classifying reviews according to their helpfulness has paramount importance to facilitate the access of truly informative content. In this context, previous studies have unveiled several aspects and architectures that are beneficial for the task of review perceived helpfulness prediction. The present work aims to further investigate the influence of the text specificity aspect, defined as the level of details conveyed in a text, with the same purpose. First, we explore an unsupervised domain adaptation approach for assigning text specificity scores for sentences from product reviews and we propose an evaluation measure named Specificity Prediction Evaluation (SPE) in order to achieve more reliable specificity predictions. Then, we present domain-oriented guidelines on how to incorporate, into a CNN architecture, either hand-crafted features based on text specificity or the text specificity prediction task as an auxiliary task in a multitask learning setting. In the experiments, the perceived helpfulness classification models embodied with text specificity showed significant higher precision results in comparison to a popular SVM baseline.

BRACIS 2021

Incorporating Text Specificity into a Convolutional Neural Network for the Classification of Review Perceived Helpfulness

text specificity

review helpfulness

opinion mining

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.

BRACIS 2020

technical paper

**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. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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Organized by C4AI, the 10th Brazilian Conference on Intelligent Systems (BRACIS) will take place from November 29th to December 3rd, 2021.

The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) missions are required to clean up the space, deorbiting the debris with a spacecraft. ADR mission planning has been investigated in the literature by means of metaheuristic approaches, focused on maximizing the amount of removed debris given the constraints of the spacecraft. The state-of-the-art approach uses an inver-over and maximal open walk algorithms to solve this problem. However, that approach fails to deal with large instances and duration constraints. This work extends the state of the art, increasing its performance and modeling all the constraints. Experimental results evidence the improvements over the original approach, including the ability to run for scenarios with thousands of debris.

An Enhanced TSP-based Approach for Active Debris Removal Mission Planning

Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, my presentation introduces an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs.

Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco

Acceptability semantics for the frameworks of weighted argumentation can satisfy up to one of the principles of Quality Precedence, Cardinality Precedence or Compensation, which are pairwise incompatible. In this paper we define two new principles: Quality Compensation and Cardinality Compensation, which are weakened versions of Quality Precedence and Cardinality Precedence, respectively. We show that these new principles are compatible with Compensation and propose two new semantics: a T-conorm-based, which can satisfy Quality Compensation and a cumulative sum-based semantics, which satisfies Cardinality Compensation.

On the Refinement of Compensation-based Semantics for Weighted Argumentation Frameworks

We introduce an interrogation mark ? in ASPIC+ languages as a plausibility operator to enhance any defeasible conclusion does not have the same status as an irrefutable one. The resulting framework, dubbed ASPIC?, is tailored to make a distinction between strong inconsistencies and weak inconsistencies. The aim is to avoid the former and to tolerate the latter. This means the extensions obtained from the ASPIC? framework are free of strong conflicts, but tolerant to weak conflicts. Then, in the current study, we show ASPIC? satisfy reasonable properties. In particular, we focus on the property that a conflict between two arguments should not interfere with the acceptability of other unrelated arguments. With this purpose in mind, we prove under which conditions the important principles of Non-interference and Crash-Resistance hold in ASPIC?.

ASPIC? and the Postulates of Non-Interference and Crash-Resistance

We propose in this paper a framework of prioritised logic programs (PLP) to represent priority information explicitly in a program. Differently of others approaches, we do not restrain the preference relation only to literals, but to sets of literals. As consequence, we can express in PLPs sophisticated forms of preferences without changing the programs or introducing new atoms to obtain artificially the intended preferences. Besides, inspired by various developments in the literature on preference, we present a comprehensive and systematic treatment to deal with preferences in logic programming. In fact, we introduced 32 different criteria (semantics) to establish preference between partial stable models as well as those semantics whose definition depends on partial stable models. We show some properties of our framework; in particular, we guarantee these semantics for PLP generalise their counterparts for logic programs without preferences.

A Systematic Approach to Define Semantics for Prioritised Logic Programs

Value-based health care management models require a precise accounting of health indexes such as risk events monitoring, clinical conditions, patient handling and outcomes. Currently this accounting is performed by manually reading and searching through electronic health records for these indexes. Our research proposes a way to make this an autonomous task that is performed by a computer using a portuguese free-text concept classifier model based on ontologies. To validate our model we tested it on digital clinical records from 191 patients under ischemic stroke care. We have selected 30 management indexes to be identified in these texts. Our model reached, on average 56,8 % of f1-score, varying from 5,83 % to 94,78 % f1-score across different management indexes.

Ontology based classification of electronic health records to support value-based health care

Tactical asset allocation is an essential method for defining a profitable portfolio for a given period. An analyst usually creates a tactical asset portfolio through technical analysis, a process subjective to the analyst's knowledge and interpretations. Another aspect that can directly influence the quality of a portfolio is the number of assets considered for analysis. Human analysts tend to focus on a pre-defined group of assets, limiting choices, and, consequently, the possibility of better results. This work proposes the Stock Network Portfolio Allocation (SNPA) algorithm for the automatic recommendation of a stock portfolio, aiming to maximize profit and minimize risk. The proposed method considers a possibly large set of assets represented as a complex network. In which the nodes represent assets, and the edges stand for the correlation between their returns. Portfolio allocation is done through a random walk on the stock network, selecting, in the end, the most visited nodes (stocks). We conducted investment simulations on Brazilian stocks from the IBrX100 index, for 24 month periods, from Jan. 2018 to Dec. 2019. We compare the results with portfolio strategies: Ibovespa index (IBOV), classic Markowitz’s mean-variance portfolio (MV), Mean Absolute Deviation (MAD) portfolio, Conditional Value at Risk (CVaR), and Hierarchical Risk Parity (HRP). The Shape Ratio (SR), Maximum Drawdown (MDD), and Cumulated Wealth (CW) were used as performance metrics. The SNPA algorithm demonstrated its effectiveness, presenting a CW of 236.3%, being 203.5% above MV portfolio; 181.7% above CVaR portfolio; 175.6% above MAD portfolio, 184.6% above IBOV index, and 165.1% above HRP. SNPA also surpassed the benchmarks considering the performance metrics SR and MDD, with values 0.67 and -1.37 respectively, the best results among the benchmarks were observed by the HRP strategy, with 0.48 in SR index, and MV with -1.39 in MDD index.

Tactical Asset Allocation through Random Walk on Stock Network

Semi-supervised learning has received attention from researchers, as it allows one to exploit the structure of unlabeled data to achieve competitive classification results with much fewer labels than supervised approaches. The Local and Global Consistency (LGC) algorithm is one of the most well-known graph-based semi-supervised (GSSL) classifiers. Notably, its solution can be written as a linear combination of the known labels. The coefficients of this linear combination depend on a parameter𝛼, determining the decay of the reward over time when reaching labeled vertices in a random walk. In this work, we discuss how removing the self-influence of a labeled instance may be beneficial, and how it relates to leave-one-out error. Moreover, we propose to minimize this leave-one-out loss with automatic differentiation. Within this framework, we propose methods to estimate label reliability and diffusion rate. Optimizing the diffusion rate is more efficiently accomplished with a spectral representation. Results show that the label reliability approach competes with robust L1-norm methods and that removing diagonal entries reduces the risk of overfitting and leads to suitable criteria for parameter selection.

Optimizing Diffusion Rate and Label Reliability in a Graph-based Semi-supervised Classifier

Context: in the field of machine learning models are trained to learn from data, however often the context at which a model is deployed changes, degrading the performances of trained models and giving rise to a problem called Concept Drift (CD), which is a change in data distribution.Motivation: CD has attracted attention in machine learning literature, with works proposing modification to well-known algorithms’ structures, ensembles, online learning and drift detection, but most of the CD literature regards classification, while regression drift is still poorly explored. Objetive: The goal of this work is to perform a comparative study of CD detectors in the context of regression. Results: we found that (i) PH, KSWIN and EDDM showed higher detection averages; (ii) the base learner has a strong impact in CD detection and (iii) the rate at which CD happens also affects the detection process. Conclusion: our experiments were executed in a framework that can easily be extended to include new CD detectors and base learners, allowing future studies to use it.

A Comparative Study on Concept Drift Detectors for Regression

Automatic fingerprint identification systems (AFIS) are among the most used people identification solutions. As the size of fingerprint databases is continuously growing, studying fingerprint indexing mechanisms is desirable to facilitate the search process in a large-scale database. This work presents a method for fingerprint indexing, which uses both exact and approximation methods of nearest neighbors (ANNs), which are very efficient in terms of runtime, even if they sacrifice a little accuracy by presenting approximate solutions. In the presented approach, searches with ANN methods are made from deep embedding vectors extracted from image databases using a convolutional neural network (CNN). In this work, a CNN ResNet18 was used to extract the deep feature embeddings vectors, and the vectors vary in size between 64, 96, and 128. The ANNs methods tested for the query step were ANNOY, NGT, HNSW, and Nanoflann. The results were quite promising when using the FVC fingerprint databases (2000, 2002, and 2004), once we reached 100% hits in the searches with a penetration rate of 1%, with very low run times.

Incorporating Text Specificity into a Convolutional Neural Network for the Classification of Review Perceived Helpfulness

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An Enhanced TSP-based Approach for Active Debris Removal Mission Planning

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