2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)最新文献_第4页

A Graph Theoretical Approach for Testing Binomiality of Reversible Chemical Reaction Networks 检验可逆化学反应网络二项性的图论方法

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00027

Hamid Rahkooy, Cristian Vargas Montero

引用次数: 1

Extractive Summarization using Cohesion Network Analysis and Submodular Set Functions 基于内聚网络分析和子模集函数的抽取摘要

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00035

Valentin Sergiu Cioaca, M. Dascalu, D. McNamara

{"title":"Extractive Summarization using Cohesion Network Analysis and Submodular Set Functions","authors":"Valentin Sergiu Cioaca, M. Dascalu, D. McNamara","doi":"10.1109/SYNASC51798.2020.00035","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00035","url":null,"abstract":"Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source ReaderBench framework, which can be retrofit to cover more than 50 languages. While considering the extensibility of the approach and the problem of missing labeled data for training in various languages besides English, an unsupervised algorithm was preferred to perform extractive summarization (i.e., select the most representative sentences from the original document). Specifically, two different approaches relying on text cohesion were implemented: a) a graph-based text representation derived from Cohesion Network Analysis that extends TextRank, and b) a class of submodular set functions. Evaluations were performed on the DUC dataset and use as baseline the implementation of TextRank from Gensim. Our results using the submodular set functions outperform the baseline. In addition, two use cases on English and Romanian languages are presented, with corresponding graphical representations for the two methods.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"103 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114134077","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

Deep Visual Domain Adaptation 深度视觉域适应

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00013

G. Csurka

引用次数: 183

Lung Tumor Segmentation Accelerated by CUDA CUDA加速肺肿瘤分割

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00047

Sorin Valcan, Mihail Gaianu

引用次数: 1

An analysis of aggregated coupling's suitability for software defect prediction 聚合耦合对软件缺陷预测的适用性分析

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00032

Diana-Lucia Miholca, Zsuzsanna Onet-Marian

{"title":"An analysis of aggregated coupling's suitability for software defect prediction","authors":"Diana-Lucia Miholca, Zsuzsanna Onet-Marian","doi":"10.1109/SYNASC51798.2020.00032","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00032","url":null,"abstract":"Software Defect Prediction is an important problem during the development of a software system, because it helps to focus the testing effort on those parts of the system which have a high probability of being defective. It is also well-researched, there being many papers presenting Machine Learning-based prediction models for this problem. But most of them use the same object-oriented structural software metrics as features. In this paper we investigate the impact of aggregated coupling, which combines structural and conceptual coupling, on software defects proneness. In this regard, we present three software metrics suites derived from both structural and conceptual coupling and analyze how their different combinations influence the performance of software defect prediction models. We analyze the relative performance of the models when using features extracted with LSI versus Doc2Vec in conjunction with Cosine versus Euclidean similarity for computing the conceptual coupling. The results suggest that all these features are complementary and their usage improves the performance of the machine learning models.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701342","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

Optimizing Deep Learning Models for Object Detection 优化对象检测的深度学习模型

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00051

Calin-George Barburescu, Gabriel Iuhasz

{"title":"Optimizing Deep Learning Models for Object Detection","authors":"Calin-George Barburescu, Gabriel Iuhasz","doi":"10.1109/SYNASC51798.2020.00051","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00051","url":null,"abstract":"Deep learning models for object detection have gotten larger and larger over the years, spanning from 3.9M trainable parameters for EfficientDet to 209M for the AmoebaNet-based NAS-FPN detector. Different strategies are currently being researched in order to improve the efficiency of deep learning models for object detection, one of which is running the training and inference of the neural network in low precision. Interesting results have been achieved by researchers, starting from the original paradigm of using operators and doing the necessary operations in IEEE single precision (FP32), to achieving similar accuracies of the models using custom minifloat formats (FP8). The results can be pushed even further by using genetic algorithms for hyperparameter tuning, in order to find specific hyperparameter for the FP8 version of the model. In this paper, we will present the results of our experiments utilizing YOLOv3 with hybrid floating-point format (HFP8). One of the experiments in this paper shows how our solution can be used for checking social distancing guidelines which is a very important topic in the current COVID-19 pandemic.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128712851","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

Detecting Individuals High in Neuroticism based on the Color Features of the Facebook Profile Picture 基于Facebook头像颜色特征的高神经质个体检测

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00053

D. Dudău, F. Sava, Andrei A. Rusu, Virgil Cervicescu

{"title":"Detecting Individuals High in Neuroticism based on the Color Features of the Facebook Profile Picture","authors":"D. Dudău, F. Sava, Andrei A. Rusu, Virgil Cervicescu","doi":"10.1109/SYNASC51798.2020.00053","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00053","url":null,"abstract":"Previous research has mostly focused on the link between the linguistic and behavioral footprints found on social media on the one hand and personality on the other. Despite the high amount of image-based contents posted and shared online and the valuable implicit information they might conceal about users' preferences and tendencies, the study of visual traces is in its infancy. The goal of the current paper is to test whether the color characteristics of the Facebook profile picture could mirror the level of neuroticism on a sample of 508 Romanian users. For this purpose, we assessed the classification performance of four machine learning algorithms having as input three sets of visual features: (1) the colorfulness, which indicates how colorful is an image; (2) the proportion of cold colors, along with the mean and standard deviation for saturation and value; (3) the emotional load, defined as pleasure, arousal, and dominance. None of the models showed good accuracy. However, this paper contributes to the literature by being part of a line of research that requires development not only in Romania but also worldwide.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121928082","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

SPIKE, an automatic theorem prover — revisited 斯派克，一个自动定理证明者——重新审视

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00025

Sorin Stratulat

引用次数: 3

AgriSen - A Dataset for Crop Classification AgriSen -一个农作物分类数据集

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00049

Teodora Selea, Marius-Florin Pslaru

引用次数: 0

Fischer-Ladner Closure for Many-Sorted Modal Logic with Application for Operational Semantics 多排序模态逻辑的fisher - ladner闭包及其在操作语义上的应用

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00022

Natalia Moanga

引用次数: 0