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Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user&#39;s interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.

BRACIS 2021

Time-Dependent Item Embeddings for Collaborative Filtering

item embeddings

distributed vector representation

collaborative filtering

temporal data

sliding window

recommender systems

**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**.

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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.

Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user's interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.

technical paper

The “neat” and “scruffy” portraits have long been painted to describe viewpoints, styles of reasoning and methodologies in AI research. Essentially, the neats defend techniques based on first principles and grounded in mathematical rigor, while the scruffies advocate diversity within cognitive architectures, sometimes meant to be models of parts of the brain, sometimes just kludges or ad-hoc pieces of engineered code. The recent success of deep learning has revived the debate between these two approaches to AI; in this context, some natural questions arise. How can we characterize, and how can we classify, these positions given the history of AI? More importantly, what is the relevance of these positions for the future of AI? How should AI research be pursued from now on, neatly or scruffly? These are the questions we address in this paper, resorting to historical analysis and to recent research trends to articulate possible ways to allocate energy so as to take the field to maximal fruition.

The future of AI: neat or scruffy?

Hierarchical data stream classification inherits the properties and constraints of hierarchical classification and data stream classification concomitantly. Therefore, it requires novel approaches that (i) can handle class hierarchies, (ii) can be updated over time, and (iii) are computationally light-weighted regarding processing time and memory usage. In this study, we propose the Gaussian Naive Bayes for Hierarchical Data Streams (GNB-hDS) method: an incremental Gaussian Naive Bayes for classifying potentially unbounded hierarchical data streams. GNB-hDS uses statistical summaries of the data stream instead of storing actual instances. These statistical summaries allow more efficient data storage, keep constant computational time and memory, and calculate the probability of an instance belonging to a specific class via the Bayes' Theorem. We compare our method against a technique that stores raw instances, and results show that our method obtains equivalent prediction rates while being significantly faster.

Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes

An effective way to cope with classification problems, among others, is by using Fuzzy Rule-Based Classification Systems (FRBCSs). These systems are composed by two main components, the Knowledge Base (KB) and the Fuzzy Reasoning Method (FRM). The FRM is responsible for performing the classification of new examples based on the information stored in the KB. A key point in the FRM is how the information given by the fired fuzzy rules is aggregated. Precisely, the aggregation function is the component that differs from the two most widely used FRMs in the specialized literature. In this paper we provide a revision of the literature discussing the generalizations of the Choquet integral that has been applied in the FRM of a FRBCS. To do so, we consider an analysis of different generalizations, by t-norms, copulas, and by F functions. Also, the main contributions of each generalization are discussed.

On the generallzations of Choquet integral for application in FRBC

Machine learning models are widespread in many different fields due to their remarkable performances in many tasks. Some require greater interpretability, which often signifies that it is necessary to understand the mechanism underlying the algorithms. Feature importance is the most common explanation and is essential in data mining, especially in applied research. There is a frequent need to compare the effect of features over time, across models, or even across studies. For this, a single metric for each feature shared by all may be more suitable. Thus, analysts may gain better first-order insights regarding feature behavior across these different scenarios. The β-coefficients of additive models, such as logistic regressions, have been widely used for this purpose. They describe the relationships among predictors and outcomes in a single number, indicating both their direction and size. However, for black-box models, there is no metric with these same characteristics. Furthermore, even the β-coefficients in logistic regression models have limitations. Hence, this paper discusses these limitations together with the existing alternatives for overcoming them, and proposes new metrics of feature importance. As with the coefficients, these metrics indicate the feature effect’s size and direction, but in the probability scale within a model-agnostic framework. An experiment conducted on openly available breast cancer data from the UCI Archive verified the suitability of these metrics, and another on real-world data demonstrated how they may be helpful in practice.

Interpreting classification models using feature importance based on marginal local effects

Mobility data analysis has received significant attention in the last few years. Enriching spatial-temporal trajectory data with semantic information, which is the definition of Multiple Aspect Trajectories, presents lots of opportunities, but also many challenges. Regarding trajectory classification, the state-of-the-art method called MASTERMovelets has shown to have the best classification accuracy over several datasets. Indeed, this method generates interpretable patterns called movelets which are the most discriminant sequences of points. Despite its increased performance, the method is computationally expensive and does not scale well, which makes its application unfeasible for large datasets. In this paper we propose a pivot based approach to reduce the search space, selecting only most promising trajectory points to extract movelets. We additionally provide a method to define a limited number of semantic dimensions for movelets. Experiments show that the proposed method is at least 50% faster for extracting the movelets, and shows a average drop of 82% of input to the classification models while keeping a similar classification accuracy level. Additionally, our scalability analysis with respect to computation time shows that the proposed method scales better than the other methods as the dataset grows in number of points, trajectories and dimensions.

Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

Important information for the design and operation of oceanic systems can be obtained by assessing local sea state parameters such as significant height, peak period and incidence direction. Techniques for motion-based inference and their possible drawbacks have been extensively discussed in the literature (their motivation coming from the simplicity of the required instrumentation when compared to traditional measuring systems), and machine learning approaches are now appearing in a few investigations. This paper addresses the estimation problem through supervised learning, using time series with the movement of a moored vessel to train neural networks models so as to estimate the sea state. Such time series are obtained through simulations, that consider a model of a spread-moored FPSO (Floating Production Storage and Offloading) platform with constant draft, out of a set of metocean conditions observed at Brazil’s Campos Basin. A sensitivity analysis for different classes of neural networks was run, based on the significant height estimation, to choose the network architecture with the best results with respect to the mean absolute error metric. That topology was trained and employed in the estimation of the remaining sea state parameter, separately. The outcomes of the proposed models were confronted with other neural networks-based methods and showed up a comparable or slightly better performance in the error metrics. A preliminary discussion of the ability of the approach to deal with some classical issues on motion-based estimation is presented.

Sea State Estimation with Neural Networks based on the Motion of a Moored FPSO subjected to Campos Basin Metocean Conditions

One important task in the COVID-19 clinical protocol involves the constant monitoring of patients to detect possible signs of insufficiency, which may eventually rapidly progress to hepatic, renal or respiratory failures. Hence, a prompt and correct clinical decision not only is critical for patients prognosis, but also can help when making collective decisions regarding hospital resource management. In this work, we present a network-based high-level classification technique to help healthcare professionals on this activity, by detecting early signs of insufficiency based on Complete Blood Count (CBC) test results. We start by building a training dataset, comprising both CBC and specific tests from a total of 2,982 COVID-19 patients, provided by a Brazilian hospital, to identify which CBC results are more effective to be used as biomarkers for detecting early signs of insufficiency. Basically, the trained classifier measures the compliance of the test instance to the pattern formation of the network constructed from the training data. To facilitate the application of the technique on larger datasets, a network reduction option is also introduced and tested. Numerical results show encouraging performance of our approach when compared to traditional techniques, both on benchmark datasets and on the built COVID-19 dataset, thus indicating that the proposed technique has potential to help medical workers in the severity assessment of patients. Especially those who work in regions with scarce material resources.

Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758.

Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays

Computational determination of protein stability upon point mutations is very useful in a wide field of applications. Reliability of such computational predictions is essential. Unfortunately, existing computational tools frequently disagree in their results. In the present study, the usage of Ensemble Learning Algorithms to aggregate the results from different stability prediction tools is investigated. Techniques of Stacking, Bagging, and Boosting as well as different Machine Learning algorithms as combiner function are explored. All the investigation is carried out in real dataset ProTherm for which experimental results are known. The proposed methodology was validated considering two different experiments according to the training set. Results show that our proposed ensemble approach is appropriate to predict the effect of point mutations on protein stability showing more reliable results than the individual tools improving overall accuracy, precision and/or recall.

Ensemble of Protein Stability Upon Point Mutation Predictors

Family farming represents a critical segment of Brazilian agriculture, involving more than 5 million properties and generating 74% of rural jobs in the country. Yield losses caused by crop diseases and pests can be devastating for small-scale producers. However, successful disease control requires correct identication, which challenges smallholders, who often lack technical assistance. The present work proposes a system that detects disease symptoms in images of plant leaves to assist phytopathology experts. The objective is to decrease the experts' workload and enable consulting services for free or at nominal cost. In addition, the required digital communication channel will promote the formation of a caring community ready to offer unpaid advice for family farmers. The machine learning and renement of the assistance system are described in detail. The developed classication system achieves a recall value of 95%.

Time-Dependent Item Embeddings for Collaborative Filtering

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