Pub Date : 2018-09-01DOI: 10.1109/synasc.2018.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/synasc.2018.00001","DOIUrl":"https://doi.org/10.1109/synasc.2018.00001","url":null,"abstract":"","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129440609","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00011
Olivier Bournez
We review several results relating ordinary differential equations and analog models of computations.
我们回顾了有关常微分方程和模拟计算模型的几个结果。
{"title":"Ordinary Differential Equations & Computability","authors":"Olivier Bournez","doi":"10.1109/SYNASC.2018.00011","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00011","url":null,"abstract":"We review several results relating ordinary differential equations and analog models of computations.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664644","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00068
Alexandru-Ion Marinescu, Z. Bálint, L. Dioşan, A. Andreica
Extending and optimizing cellular automata to handle 3D volume segmentation is a non-trivial task. First, it does not suffice to simply alter the cell neighborhood (be it von Neumann or Moore), and second, going from 2D to 3D means that the number of operations increases by an order of magnitude, thus GPU acceleration becomes a necessity, advantage inherent to cellular automata approaches. When discussing 3D medical imagistics, we mean that the entire stack of slices from a certain sequence within an acquisition is stored as a single entity. This, in turn, enables us to accurately segment whole volumes in a single run, which would otherwise need per-slice segmentation followed by a stitching post-process. This paper focuses mainly on a thorough benchmark analysis of the 3D Unsupervised Grow Cut technique. We discuss algorithm speed of convergence, stability and behavior with respect to global meta-parameters such as segmentation threshold, keeping track of output quality metrics as the algorithm unfolds. Our end goal is to segment the heart cavities from cardiac MRI and to yield an interactive 3D reconstruction which can be easily handled and analyzed by the radiologist.
{"title":"Unsupervised and Fully Autonomous 3D Medical Image Segmentation Based on Grow Cut","authors":"Alexandru-Ion Marinescu, Z. Bálint, L. Dioşan, A. Andreica","doi":"10.1109/SYNASC.2018.00068","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00068","url":null,"abstract":"Extending and optimizing cellular automata to handle 3D volume segmentation is a non-trivial task. First, it does not suffice to simply alter the cell neighborhood (be it von Neumann or Moore), and second, going from 2D to 3D means that the number of operations increases by an order of magnitude, thus GPU acceleration becomes a necessity, advantage inherent to cellular automata approaches. When discussing 3D medical imagistics, we mean that the entire stack of slices from a certain sequence within an acquisition is stored as a single entity. This, in turn, enables us to accurately segment whole volumes in a single run, which would otherwise need per-slice segmentation followed by a stitching post-process. This paper focuses mainly on a thorough benchmark analysis of the 3D Unsupervised Grow Cut technique. We discuss algorithm speed of convergence, stability and behavior with respect to global meta-parameters such as segmentation threshold, keeping track of output quality metrics as the algorithm unfolds. Our end goal is to segment the heart cavities from cardiac MRI and to yield an interactive 3D reconstruction which can be easily handled and analyzed by the radiologist.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261982","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}
Pub Date : 2018-09-01DOI: 10.1109/synasc.2018.00080
{"title":"[Publisher's information]","authors":"","doi":"10.1109/synasc.2018.00080","DOIUrl":"https://doi.org/10.1109/synasc.2018.00080","url":null,"abstract":"","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131433129","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00074
Diana-Lucia Miholca
Software defect prediction is an intricate but essential software testing related activity. As a solution to it, we have recently proposed HyGRAR, a hybrid classification model which combines Gradual Relational Association Rules (GRARs) with ANNs. ANNs were used to learn gradual relations that were then considered in a mining process so as to discover the interesting GRARs characterizing the defective and non-defective software entities, respectively. The classification of a new entity based on the discriminative GRARs was made through a non-adaptive heuristic method. In current paper, we propose to enhance HyGRAR through autonomously learning the classification methodology. Evaluation experiments performed on two open-source data sets indicate that the enhanced HyGRAR classifier outperforms the related approaches evaluated on the same two data sets.
{"title":"An Improved Approach to Software Defect Prediction using a Hybrid Machine Learning Model","authors":"Diana-Lucia Miholca","doi":"10.1109/SYNASC.2018.00074","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00074","url":null,"abstract":"Software defect prediction is an intricate but essential software testing related activity. As a solution to it, we have recently proposed HyGRAR, a hybrid classification model which combines Gradual Relational Association Rules (GRARs) with ANNs. ANNs were used to learn gradual relations that were then considered in a mining process so as to discover the interesting GRARs characterizing the defective and non-defective software entities, respectively. The classification of a new entity based on the discriminative GRARs was made through a non-adaptive heuristic method. In current paper, we propose to enhance HyGRAR through autonomously learning the classification methodology. Evaluation experiments performed on two open-source data sets indicate that the enhanced HyGRAR classifier outperforms the related approaches evaluated on the same two data sets.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115505172","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00078
Kristian Miok
Currently there are various methods allowing the construction of predictive models based on data. Measuring prediction uncertainty plays an essential role in fields such as medicine, physics and biology where the information about prediction accuracy can be essential. In this context only a few approaches address the question of how much the predicted values can be trusted. Neural networks are popular models, but unlike the statistical models, they do not quantify the uncertainty involved in the prediction process. In this work we investigate several regression models with a focus on estimating prediction intervals that statistical and machine learning models can provide. The analysis is conducted for a case study aiming to predict the number of crayfish in Romanian rivers based on landscape and water quality information.
{"title":"Estimation of Prediction Intervals in Neural Network-Based Regression Models","authors":"Kristian Miok","doi":"10.1109/SYNASC.2018.00078","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00078","url":null,"abstract":"Currently there are various methods allowing the construction of predictive models based on data. Measuring prediction uncertainty plays an essential role in fields such as medicine, physics and biology where the information about prediction accuracy can be essential. In this context only a few approaches address the question of how much the predicted values can be trusted. Neural networks are popular models, but unlike the statistical models, they do not quantify the uncertainty involved in the prediction process. In this work we investigate several regression models with a focus on estimating prediction intervals that statistical and machine learning models can provide. The analysis is conducted for a case study aiming to predict the number of crayfish in Romanian rivers based on landscape and water quality information.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126212893","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}
Pub Date : 2018-09-01DOI: 10.1109/synasc.2018.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/synasc.2018.00003","DOIUrl":"https://doi.org/10.1109/synasc.2018.00003","url":null,"abstract":"","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130389887","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00060
Sebastian-Aurelian Ștefănigă, Mihail Gaianu
One of the objectives of deep learning is to solve high complex tasks such as perception. In recent years, it has been demonstrated that deep learning can overcome traditional algorithms in image classification as well as object recognition and face recognition tasks. In this paper we are inspecting techniques of deep learning that deals with topical issues in the field of Computer Vision: real-time face detection and face recognition using embedded system and GPU processing on NVidia Tegra X2 (Jetson TX2). In the first part of our work we are proposing a novel experimental research to the problem of face detection and recognition in autonomous driving that use a new deep convolutional neural network model, named FADNet. The architecture model was used on a existing dataset containing more then 13.000 images of 2.000 different faces from different cultures, on which we gained an accuracy of 81.78%, along with an accuracy of 84.45% on a detection dataset containing new 8.600 images. In the final phase of the experimental research we did a real-time test on a dataset of self-acquired video frames from Jetson TX2 embedded system camera, achieving an accuracy of 67.45%, which is a promising result for real-time processing. Last but not least, accuracy and inference time are taken into account by comparing time performance between CPU and GPU implementations.
{"title":"Face Detection and Recognition Methods using Deep Learning in Autonomous Driving","authors":"Sebastian-Aurelian Ștefănigă, Mihail Gaianu","doi":"10.1109/SYNASC.2018.00060","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00060","url":null,"abstract":"One of the objectives of deep learning is to solve high complex tasks such as perception. In recent years, it has been demonstrated that deep learning can overcome traditional algorithms in image classification as well as object recognition and face recognition tasks. In this paper we are inspecting techniques of deep learning that deals with topical issues in the field of Computer Vision: real-time face detection and face recognition using embedded system and GPU processing on NVidia Tegra X2 (Jetson TX2). In the first part of our work we are proposing a novel experimental research to the problem of face detection and recognition in autonomous driving that use a new deep convolutional neural network model, named FADNet. The architecture model was used on a existing dataset containing more then 13.000 images of 2.000 different faces from different cultures, on which we gained an accuracy of 81.78%, along with an accuracy of 84.45% on a detection dataset containing new 8.600 images. In the final phase of the experimental research we did a real-time test on a dataset of self-acquired video frames from Jetson TX2 embedded system camera, achieving an accuracy of 67.45%, which is a promising result for real-time processing. Last but not least, accuracy and inference time are taken into account by comparing time performance between CPU and GPU implementations.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"125 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120841447","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00030
A. Alexandru, Gabriel Ciobanu
We present some properties of the order relations in the framework of finitely supported structures. We particularly analyze partially ordered sets, lattices and Galois connections, presenting specific properties (regarding cardinality order, cardinality arithmetic and fixed points) in the framework of finitely supported algebraic structures, as well as properties that are naturally extended from the classical Zermelo-Fraenkel framework by replacing 'structure' with 'atomic finitely supported structure'.
{"title":"Order Relations Over Finitely Supported Structures","authors":"A. Alexandru, Gabriel Ciobanu","doi":"10.1109/SYNASC.2018.00030","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00030","url":null,"abstract":"We present some properties of the order relations in the framework of finitely supported structures. We particularly analyze partially ordered sets, lattices and Galois connections, presenting specific properties (regarding cardinality order, cardinality arithmetic and fixed points) in the framework of finitely supported algebraic structures, as well as properties that are naturally extended from the classical Zermelo-Fraenkel framework by replacing 'structure' with 'atomic finitely supported structure'.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876775","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}
Pub Date : 2018-09-01DOI: 10.1109/SYNASC.2018.00071
Adrian Ioan Pîrîu, M. Leonte, Nicolae Postolachi, Dragos Gavrilut
Starting from 2005-2006 the number of malware samples had an exponential growth to a point where at the beginning of 2018 more than 800 million samples were known. With these changes, security vendors had to adjust - one solution being using machine learning algorithms for prediction. However, as the malware number grows so should the benign sample set (if one wants to have a reliable training and a proactive model). This paper presents some key aspects related to procedures and optimizations one needs to do in order to create a large cleanset (a collection of benign files) that can be used for machine learning training.
{"title":"Optimizing Cleanset Growth by Using Multi-Class Neural Networks","authors":"Adrian Ioan Pîrîu, M. Leonte, Nicolae Postolachi, Dragos Gavrilut","doi":"10.1109/SYNASC.2018.00071","DOIUrl":"https://doi.org/10.1109/SYNASC.2018.00071","url":null,"abstract":"Starting from 2005-2006 the number of malware samples had an exponential growth to a point where at the beginning of 2018 more than 800 million samples were known. With these changes, security vendors had to adjust - one solution being using machine learning algorithms for prediction. However, as the malware number grows so should the benign sample set (if one wants to have a reliable training and a proactive model). This paper presents some key aspects related to procedures and optimizations one needs to do in order to create a large cleanset (a collection of benign files) that can be used for machine learning training.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130251121","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：