2018 7th Brazilian Conference on Intelligent Systems (BRACIS)最新文献_第3页

Efficient Width-Extended Convolutional Neural Network for Robust Face Spoofing Detection 高效宽扩展卷积神经网络鲁棒人脸欺骗检测

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00047

G. Souza, D. F. S. Santos, R. G. Pires, J. Papa, A. Marana

{"title":"Efficient Width-Extended Convolutional Neural Network for Robust Face Spoofing Detection","authors":"G. Souza, D. F. S. Santos, R. G. Pires, J. Papa, A. Marana","doi":"10.1109/BRACIS.2018.00047","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00047","url":null,"abstract":"Biometrics has been increasingly used as a safe and convenient technique for people identification. Despite the higher security of biometric systems, criminals have already developed methods to circumvent them, being the presentation of fake biometric information to the input sensor (spoofing attack) the easiest way. Face is considered one of the most promising biometric traits for people identification, including in mobile devices. However, face recognition systems can be easily fooled, for instance, by presenting to the sensor a printed photograph, a 3D mask, or a video recorded from the face of a legal user. Recently, despite some CNNs (Convolutional Neural Networks) based approaches have achieved state-of-the-art results in face spoofing detection, in most of the cases the proposed architectures are very deep, being unsuitable for devices with hardware restrictions. In this work, we propose an efficient architecture for face spoofing detection based on a width-extended CNN, which we called wCNN. Each part of wCNN is trained, separately, in a given region of the face, then their outputs are combined in order to decide whether the face presented to the sensor is real or fake. The proposed approach, which learns deep local features from each facial region due to its width-wide architecture, presented better accuracy than state-of-the-art methods, including the well-referenced fine-tuned VGG-Face, while being much more efficient regarding hardware resources and processing time.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121516959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Nonlinear Discriminant Principal Component Analysis for Image Classification and Reconstruction 非线性判别主成分分析在图像分类与重建中的应用

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00061

T. Filisbino, G. Giraldi, C. Thomaz

引用次数: 1

A Hyper-Heuristic Collaborative Multi-objective Evolutionary Algorithm 一种超启发式协同多目标进化算法

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00068

G. Fritsche, A. Pozo

{"title":"A Hyper-Heuristic Collaborative Multi-objective Evolutionary Algorithm","authors":"G. Fritsche, A. Pozo","doi":"10.1109/BRACIS.2018.00068","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00068","url":null,"abstract":"Many-objective optimization problems (MaOPs) are a great challenge for multi-objective evolutionary algorithms (MOEAs) and lately, several MOEAs have been proposed. Each MOEA uses different algorithmic components during the search process and performs differently. Therefore, there is no single algorithm able to achieve the best results in all problems. The collaboration of multiple MOEAs and the use of hyperheuristics can help to create a searchability able to achieve good results in a wide range of problem instances. In this context, this research proposes a model for collaboration of MOEAs guided by hyper-heuristic, called HHcMOEA. In HHcMOEA, the hyper-heuristic controls and mix MOEAs, automatically deciding which one to apply during the search process. On the other hand, HHcMOEA also incorporates exchange of information between the MOEAs. And, a fitness improvement rate metric, based on the R2 indicator to decide about the quality of the application of an MOEA. HHcMOEA is implemented using a set of MOEAs with diverse characteristics. An experiment is used to evaluate HHcMOEA in two versions: with and without information exchange. Although, the two versions of HHcMOEA are compared to the MOEAs applied alone. The empirical evaluation used a set of benchmark problems with different properties. The proposed model achieved the best result or equivalent to the best in almost all problems. Still, the results were deteriorated when the information exchange strategy was not used.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128364364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Cartesian Genetic Programming with Crossover for Designing Combinational Logic Circuits 组合逻辑电路设计中的交叉笛卡尔遗传规划

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00033

J. E. H. D. Silva, H. Bernardino

引用次数: 9

Advances in Automatically Solving the ENEM ENEM自动求解的研究进展

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00016

Igor Cataneo Silveira, Denis Deratani Mauá

{"title":"Advances in Automatically Solving the ENEM","authors":"Igor Cataneo Silveira, Denis Deratani Mauá","doi":"10.1109/bracis.2018.00016","DOIUrl":"https://doi.org/10.1109/bracis.2018.00016","url":null,"abstract":"Answering questions formulated in natural language is a long standing quest in Artificial Intelligence. However, even formulating the problem in precise terms has proven to be too challenging, which lead many researchers to focus on Multiple-Choice Question Answering problems. One particularly interesting type of the latter problem is solving standardized tests such as university entrance exams. The Exame Nacional do Ensino Médio (ENEM) is a High School level exam widely used by Brazilian universities as entrance exam, and the world's second biggest university entrance examination in number of registered candidates. In this work we tackle the problem of answering purely textual multiple-choice questions from the ENEM. We build on a previous solution that formulated the problem as a text information retrieval problem. In particular, we investigate how to enhance these methods by text augmentation using Word Embedding and WordNet, a structured lexical database where words are connected according to some relations like synonymy and hypernymy. We also investigate how to boost performance by building ensembles of weakly correlated solvers. Our approaches obtain accuracies ranging from 26% to 29.3%, outperforming the previous approach.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121234129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Synthesis of a DNF Formula From a Sample of Strings 从字符串样本合成DNF公式

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00098

T. Rocha, Ana Teresa C. Martins, F. Ferreira

引用次数: 4

Increasing Convolutional Neural Networks Training Speed by Incremental Complexity Learning 增量复杂度学习提高卷积神经网络训练速度

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00026

Miguel D. de S. Wanderley, R. Prudêncio

引用次数: 0

Bayesian Classifiers Supported by Ranking for Decision Making in Robot Soccer 基于排名支持的贝叶斯分类器在机器人足球决策中的应用

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00081

Rodrigo Caputo, Edmilson Santos

引用次数: 0

MAVIS: A Multiagent Value Investing System MAVIS:一个多主体价值投资系统

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00071

Everton Rodrigues Reis, Jaime Simão Sichman

引用次数: 0

Effects of Population Initialization on Evolutionary Techniques for Subgroup Discovery in High Dimensional Datasets 种群初始化对高维数据集子群发现进化技术的影响

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00013

Vitor de Albuquerque Torreao, Renato Vimieiro

{"title":"Effects of Population Initialization on Evolutionary Techniques for Subgroup Discovery in High Dimensional Datasets","authors":"Vitor de Albuquerque Torreao, Renato Vimieiro","doi":"10.1109/BRACIS.2018.00013","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00013","url":null,"abstract":"Many Evolutionary Algorithms have been proposed to solve the Subgroup Discovery task. Some of these, however, have been shown to work poorly in high dimensional problems. The best performing evolutionary algorithm for subgroup discovery in high dimensional datasets has a particular way to initialize its starting population, limiting the size of initial solutions to the lowest possible value. As with most population-based techniques, the outcome of evolutionary algorithms is usually dependent on the initial set of solutions, which are typically randomly generated. The impact of choosing one initialization technique over another in the final presented solution has been the topic of many published works in the broad area of evolutionary computation. However, to the best of our knowledge, it has not been the topic of study in the specific case of the Subgroup Discovery task, especially when considering high dimensional datasets. Therefore, this paper aims at studying whether or not it is possible to improve the performance of evolutionary algorithms in high dimensional subgroup discovery tasks by biasing the initial population to individuals with lower sizes.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":" 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133121923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3