2018 7th Brazilian Conference on Intelligent Systems (BRACIS)最新文献_第2页

Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning 基于批强化学习的基因调控网络吸引盆地控制

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00030

Cyntia Eico Hayama Nishida, Anna Helena Reali Costa, R. Bianchi

{"title":"Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning","authors":"Cyntia Eico Hayama Nishida, Anna Helena Reali Costa, R. Bianchi","doi":"10.1109/BRACIS.2018.00030","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00030","url":null,"abstract":"Basin of attraction contains biological functions and channels cell behavior, so when a gene network is in an unhealthy basin it may cause diseases. Control techniques can support the design of therapies that promote the transition of a biological system from diseased to healthier basins. Most control methods first infer a gene network and then derive a control strategy to avoid diseased states. However, this approach is limited to few genes and may cause other diseases, as the biological side of the problem is not considered. While changing between basins may change a diseased biological function for a healthier one, state avoidance can change functions in an unexpected way. We propose to extend a batch reinforcement learning method FQI-Sarsa, to change basin of attractions in a partial observable network. Using a batch reinforcement learning technique avoids the most time consuming phases that are the inference and control of the gene network. Results demonstrate that our method, BOAFQI-Sarsa, is more effective than previous studies that do not consider basins in their computations.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"6 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":"120972818","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: 2

Fusion of Local Descriptors for Multi-view Facial Expression Recognition 多视角面部表情识别的局部描述符融合

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00104

Xuejian Wang, M. Fairhurst, A. Canuto

{"title":"Fusion of Local Descriptors for Multi-view Facial Expression Recognition","authors":"Xuejian Wang, M. Fairhurst, A. Canuto","doi":"10.1109/BRACIS.2018.00104","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00104","url":null,"abstract":"Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.","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":"116703874","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: 0

A Study of Biclustering Coherence Measures for Gene Expression Data 基因表达数据的双聚类一致性测度研究

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00100

V. A. Padilha, A. Carvalho

引用次数: 3

Dimensionality Reduction for the Algorithm Recommendation Problem 算法推荐问题的降维方法

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00062

Edesio Alcobaça, R. G. Mantovani, A. L. Rossi, A. Carvalho

引用次数: 2

Estimation of the Energy Production in a Wind Farm Using Regression Methods and Wind Speed Forecast 基于回归方法和风速预报的风电场发电量估算

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00022

P. P. Rebouças Filho, Navar de Medeiros Mendonça e Nascimento, Shara Shami Araújo Alves, Samuel Luz Gomes, Cláudio Marques de Sá Medeiros

{"title":"Estimation of the Energy Production in a Wind Farm Using Regression Methods and Wind Speed Forecast","authors":"P. P. Rebouças Filho, Navar de Medeiros Mendonça e Nascimento, Shara Shami Araújo Alves, Samuel Luz Gomes, Cláudio Marques de Sá Medeiros","doi":"10.1109/bracis.2018.00022","DOIUrl":"https://doi.org/10.1109/bracis.2018.00022","url":null,"abstract":"Wind energy is an excellent source of alternative energy to complement the Brazilian energy matrix. However, one of the significant challenges lies in managing this resource, due to its intermittent behavior. This study addresses the estimation of the electric power production of the wind park, so its management could be more efficient. A real data from one-year records of wind speed and power from a wind park installed in a wind farm in Ceará State, Brazil, is used. At first, we provide a study of Logistic versus Least Squares regression to model the wind turbine power curve. Then, a novel variant of the Least Square Support Regression is used to forecast wind speed on the site. The Logistic regression demonstrated to be more suitable for the task of regression, and the wind speed forecasting with three steps ahead provided lower error rates. Our approach represents a system based on data from both wind turbine power and speed to serve as a tool for helping energy selling issues and scheduling turbine maintenance on periods of time with low energy production in the wind park.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 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":"115183348","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

[Publisher's information] (发布者的信息)

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00106

引用次数: 0

Deep Learning for People Detection on Beach Images 基于深度学习的海滩图像人物检测

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00045

S. Chevtchenko, Rafaella F. Vale, F. Cordeiro, V. Macário

{"title":"Deep Learning for People Detection on Beach Images","authors":"S. Chevtchenko, Rafaella F. Vale, F. Cordeiro, V. Macário","doi":"10.1109/BRACIS.2018.00045","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00045","url":null,"abstract":"Convolutional architectures have in recent years become state-of-the-art for several object detection tasks. However, these detectors have not yet been evaluated for detection and monitoring of beach areas. As some of these areas need to be continually monitored for dangerous situations, such as shark attacks, an automated system would be an effective risk control measure. The most significant and specific challenges for this problem are variable scene illumination, partial occlusion and distant camera position. In this work we present a study on three recent convolutional architectures for the task of people detection in beach scenarios. Our dataset is composed of images taken in the Boa Viagem beach, in Brazil, and is used to evaluate Faster R-CNN, R-FCN and SSD in terms of quality and speed of detection. The detectors are pretrained on a dataset containing 91 classes of objects, including people with different levels of scale and occlusion. The results suggest that the Faster R-CNN meta-architecture with the Resnet 101 feature extractor generates significantly better detections in terms of F-measure, while performing at 5.6 fps on a GTX 1080 Ti GPU.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"27 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":"128039211","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: 6

Benchmarking Multi-target Regression Methods 基准多目标回归方法

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00075

Saulo Martiello Mastelini, Everton José Santana, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior

引用次数: 5

Making Data Stream Classification Tree-Based Ensembles Lighter 使基于数据流分类树的集成更轻

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00089

V. G. T. D. Costa, S. M. Mastelini, A. Carvalho, Sylvio Barbon Junior

{"title":"Making Data Stream Classification Tree-Based Ensembles Lighter","authors":"V. G. T. D. Costa, S. M. Mastelini, A. Carvalho, Sylvio Barbon Junior","doi":"10.1109/BRACIS.2018.00089","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00089","url":null,"abstract":"Recently, several classification algorithms capable of dealing with potentially infinite data streams have been proposed. One of the main challenges of this task is to continuously update predictive models to address concept drifts without compromise their predictive performance. Moreover, the classification algorithm used must be able to efficiently deal with processing time and memory limitations. In the data stream mining literature, ensemble-based classification algorithms are a good alternative to satisfy the previous requirements. These algorithms combine multiple weak learner algorithms, e.g., the Very Fast Decision Tree (VFDT), to create a model with higher predictive performance. However, the memory costs of each weak learner are stacked in an ensemble, compromising the limited space requirements. To manage the trade-off between accuracy, memory space, and processing time, this paper proposes to use the Strict VFDT (SVFDT) algorithm as an alternative weak learner for ensemble solutions which is capable of reducing memory consumption without harming the predictive performance. This paper experimentally compares two traditional and three state-of-the-art ensembles using as weak learners the VFDT and SVFDT across thirteen benchmark datasets. According to the experimental results, the proposed algorithm can obtain a similar predictive performance with a significant economy of memory space.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"71 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120968808","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: 7

Data Classification: Dimensionality Reduction Using Combined and Non-combined Multidimensional Projection Techniques 数据分类:使用组合和非组合多维投影技术的降维

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00076

G. E. Rodrigues, Wilson Estécio Marcílio Júnior, D. M. Eler

引用次数: 1