Brazil

The prediction of secondary protein structures is one of the classic problems of bioinformatics and has several practical applications.In this work, we present an ensemble of bidirectional recurrent networks with random forests. The fusion was performed using weights for each of the classifiers and classes, found by searching through genetic algorithms. To evaluate our method, we used the PDB, CB6133 and CB513 datasets. We achieved 73.1%, 59.1% and 55.8% Q8 accuracy on PDB, CB6133 and CB513 proteins, respectively, whereas 81.5% Q3 accuracy on PDB proteins using only amino acid sequence information. In order to compare our results against the literature, we also evaluated our method using amino acid sequence and sequence profile features, achieving 73.4% and 68.9% Q8 accuracy on CB6133 and CB513. Our method yielded good results when using only the amino acid sequence and presents competitive results compared tothe literature when using amino acid sequence information and protein sequence similarity.

IWSSIP 2020

Ensemble of Bidirectional Recurrent Networks and Random Forests for Protein Secondary Structure Prediction

recurrent network

protein secondary structure prediction

random forest

ensemble

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

To make possible the exploration of oil and natural gas in maritime waters, it is necessary to develop high technology to support the projects of drilling and exploration of submarine wells. One of the equipment that enables offshore exploration is the flexible pipes. Considering the high cost and complexity of the flexible pipe manufacturing process, the objective of this work is to analyze the effect of the occurrence of whitening stress identified as bleaching observed in the polymeric material known as Polyfluoride of Vinylidene (PVDF), which is used in the manufacture of inner liner layers in flexible pipes. The proposed methodology is to develop a prediction model based on classification, which evaluates the whitening bleaching by extracting knowledge from an image database. Through the proposed methodology, the most relevant parameters associated with the study problem that can define classification rules areidentified. As a result, some relationships between some characters, fruits of image data extraction and product characteristics were identified through decision trees. The conclusion is that these relationships provides support for decision making when peformed by a domain expert.

Analysis of Non-Conformity in Flexible Pipe through Data Mining from Image Bank

The Neutrinos Angra Experiment aims at monitoring nuclear plants by detecting antineutrino particles coming out from its fuel burn-up process. The Neutrinos Angra Collaboration developed a fully-equipped detector to accomplish this task. It
is currently in operation on the surface and next to the dome of the Angra II nuclear reactor. Selecting antineutrinos events on the surface is a challenge task due to the high level of background noise produced by cosmic ray particles. One of the
main parameters used to select antineutrino events is the number of photons acquired by the detector’s sensors. This quantity is estimated based on the signals generated at the output of the readout electronics. If any of those signals is corrupted, the estimation fails, compromising the Experiment performance. This
work proposes a study of the performance of some classifier algorithms applied to identify corrupted signals for the Neutrinos Angra Experiment. If corrupted signals can be identified, they could go through a recovering process in order to improve the Experiment’s events-selection algorithms.

Identification and Classification of Corrupted Signals for the Neutrinos Angra Experiment

We discuss the use of Associative Rule Learning (ARL) in the interface design of a 3D interactive visualization of temporal medical data. Our results indicate that ARL can be a valuable resource to improve the design of Information Visualization interface tools as well as to customize visualizations according to the user´s preferences.

Machine Learning in Information Visualization: Using Rule-based Learning Algorithms to Personalize Interfaces

The Neutrinos Angra Experiment aims at developing a system capable of monitoring the fuel-burning process of nuclear reactors. This system is based on an antineutrino detector located a few meters far from the core of the Angra II nuclear reactor. One of the main parameters used to select antineutrino events is the released energy left by a particle when interacting with the detector medium which, in turn, is proportional to the amplitude and area of the signals generated by the detector’s transducers. However, the estimation of such quantities may lose accuracy
due to saturation effects caused by the experiment electronics, making it necessary to apply methods to compensate or correct such effects. This work proposes to implement and test machine learning algorithms designed to estimate the energy of saturated signals.

Amplitude Estimators Applied to Saturated Signals for the v-Angra Experiment

This work presents a technique of working together to accumulate learning between various artificial neural networks, the technique has been successfully adapted to a convolutional network and has shown promising results in the context of traffic light classification.

Optimizing CNN's using Cumulative Learning with Auxiliary Networks for Traffic Light Recognition

This presentation shows a study about the feasibility of combining sustained vowels for computer-based pathological voice characterization. Although recent work has shown that single sustained vowels enable accurate voice characterization, the literature lacks evidence about combining them. Here we propose a pipeline for pathological voice characterization and classification that encompasses a feature extraction method based on the Haar wavelet. We also evaluate the benefits of our pipeline when using an input signal that comprises three different sustained vowels instead of single ones. We conducted experiments on samples of sustained vowels /a/, /i/, and /u/ from two public datasets, exploring the wavelet decomposition levels in the range of 4 to 18.

Combined Sustained Vowels Improve the Performance of the Haar Wavelet for Pathological Voice Characterization

The skin is an essential system for the human body, also the most susceptible to diseases. The diagnosis of these diseases is made mainly through dermatoscopy images, a noninvasive procedure. This paper presents a semi-automatic method that can assist the physician in monitoring the skin lesion’s evolution. Our approach applies the SLIC0 algorithm for superpixels generation, and texture features extraction. Subsequently, the algorithm Seeded Fuzzy C-means clusters the superpixels based on marked regions made by a specialist. Finally, the images undergo a post-processing step, eliminating noise and smoothing edges. We used 1380 images from four public datasets: the PH2 and DermIs to settle the method parameters, and ISIC 2016 and ISIC 2017 to the performance evaluation. Our tests show that by marking 1% of the superpixels, our method achieves an accuracy of 96.16% in ISIC 2016 and 95.34% in ISIC 2017. According to the literature, the segmentation was considered excellent in 93.49% of the images in ISIC 2016 and on 79.44% of ISIC 2017 images.

A Skin Lesion Semi-Supervised Segmentation Method

The chest radiography is one of the most accessible radiological examinations for diagnosis of lung and heart diseases. Deep learning techniques have been increasingly used to provide more accurate detection of thorax lesions on Chest X-Ray (CXR) images. However, we observe that we can use the complementarity of dual asymmetric deep convolutional neural networks (DCNNs) to improve the ability of CXR image classification compared to the single network. In this paper, we propose a novel dual lesion attention network named DuaLAnet for the classification of 14 thorax diseases on chest radiography. The DuaLAnet consists of two asymmetric attention networks, DenseNet-169 and ResNet-152, to integrate the advantages into a wider architecture, thus extracting more discriminative features of different abnormalities from the raw CXRs. Moreover, a training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss.

DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays

In prostate analysis clinical routine, manual and semi-manual techniques are used to interpret MRI scans, which in addition to being subjective, are time-consuming and operator-dependent. Prostate segmentation is necessary for the diagnosis of a possible tumor, biopsy, staging, monitoring and treatment. Thus, an automatic segmentation can be crucial for fast and reliable diagnosis, but is challenging to accomplish due to the varying sizes, shapes and also unclear prostate boundaries that are blended with surrounding tissues. Thereupon, this work proposes an automatic method for prostate segmentation on 3D MRI scans based on superpixels, taxonomic indexes, probabilistic atlas and the XGBoost algorithm. In addition, the proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a dice similarity coefficient of 84.03%, volumetric similarity of 95.42%, recall of 88.11%, specificity of 90.82%, and an accuracy of 90.06\%. Experimental results demonstrate the high potential of the proposed method comparable to those previously published.

Taxonomic Indexes for Automatic Prostate Segmentation on 3D MRI Scans Using Superpixels and Probabilistic Atlas

Precision agriculture (PA) has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farmers to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among them, which makes the alignment process complex. In this paper, is proposed a new framework for the band co-registration process based on the premise that natural misalignment is an attribute of the camera, so it does not change during the acquisition process. The results were compared with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of $0.425$ pixels, this result being $335\%$ better than the evaluated frameworks.

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Analysis of Non-Conformity in Flexible Pipe through Data Mining from Image Bank

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