2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)最新文献_第3页

Steel Phase Kinetics Modeling using Symbolic Regression 钢相动力学的符号回归模型

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00059

David Piringer, Bernhard Bloder, G. Kronberger

引用次数: 0

Cerebral Metastases Segmentation using Transfer Gliomas Learning and GrabCut 转移胶质瘤学习和GrabCut的脑转移瘤分割

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00062

Ciprian-Mihai Ceauşescu, B. Alexe

引用次数: 0

Detecting Implicit Indeterminates in Symbolic Computation 符号计算中隐式不定式的检测

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00016

S. Watt

引用次数: 0

Reducing Adversarial Vulnerability Using GANs 利用gan减少对抗性脆弱性

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00064

Ciprian-Alin Simion

引用次数: 0

Advantages of a neuro-symbolic solution for monitoring IT infrastructures alerts 用于监控IT基础设施警报的神经符号解决方案的优点

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00036

D. Onchis, C. Istin, Eduard Hogea

引用次数: 1

Pedestrian Trajectory Prediction Based on Tree Method using Graph Neural Networks 基于图神经网络树形法的行人轨迹预测

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00046

Bogdan Ilie Sighencea

{"title":"Pedestrian Trajectory Prediction Based on Tree Method using Graph Neural Networks","authors":"Bogdan Ilie Sighencea","doi":"10.1109/SYNASC57785.2022.00046","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00046","url":null,"abstract":"Pedestrian trajectory prediction in real-world scenarios is a challenging task for several computer vision applications, such as autonomous driving, video surveillance, and robotic systems. This is not a trivial task due to the numerous potential trajectories. In this article, it provides a tree-based approach to handle this multimodal prediction challenge. The tree is designed based on the observed data and is also used to predict future trajectories. In particular, an individual's potential future trajectory is represented by the tree's root-to-leaf route. Compared to previous approaches that use implicit latent variables to describe possible future paths, the movement behaviors may be directly represented by the path in the tree (e.g., go straight and then turn left), and thus offer more socially suitable trajectories. The experimental results on the ETH, UCY, and Stanford Drone datasets show that this approach can exceed the performance of the state-of-the-art approaches. The solution is more efficient and compact, with a smaller model size and a higher accuracy, and delivers better results with reference to average displacement error (ADE) and final displacement error (FDE) metrics.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"136 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125812589","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

On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs 利用U-Net结构中的感知损失对传统图案纺织品进行语义喷漆

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00051

C. Stoean, N. Bačanin, R. Stoean, L. Ionescu, C. Alecsa, M. Hotoleanu, Miguel A. Atencia Ruiz, G. Joya

{"title":"On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs","authors":"C. Stoean, N. Bačanin, R. Stoean, L. Ionescu, C. Alecsa, M. Hotoleanu, Miguel A. Atencia Ruiz, G. Joya","doi":"10.1109/SYNASC57785.2022.00051","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00051","url":null,"abstract":"It is impressive when one gets to see a hundreds or thousands years old artefact exhibited in the museum, whose appearance seems to have been untouched by centuries. Its restoration had been in the hands of a multidisciplinary team of experts and it had undergone a series of complex procedures. To this end, computational approaches that can support in deciding the most visually appropriate inpainting for very degraded historical items would be helpful as a second objective opinion for the restorers. The present paper thus attempts to put forward a U-Net approach with a perceptual loss for the semantic inpainting of traditional Romanian vests. Images taken of pieces from the collection of the Oltenia Museum in Craiova, along with such images with garments from the Internet, have been given to the deep learning model. The resulting numerical error for inpainting the corrupted parts is adequately low, however the visual similarity still has to be improved by considering further possibilities for finer tuning.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131969322","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

What’s New In QBF Solving? : (Invited Talk) QBF求解有什么新进展?:(特邀演讲)

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00012

M. Seidl

引用次数: 0

The Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms 卷积神经网络参数对乳房x线影像二值分类的影响

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00035

Mădălina Dicu, L. Dioşan, A. Andreica, C. Chira, Alin Cordoş

{"title":"The Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms","authors":"Mădălina Dicu, L. Dioşan, A. Andreica, C. Chira, Alin Cordoş","doi":"10.1109/SYNASC57785.2022.00035","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00035","url":null,"abstract":"Breast cancer is the most commonly diagnosed type of cancer. It is essential to classify patients as quickly as possible into groups with a high or low risk of cancer, to provide adequate treatment. This paper aims to address the impact of the parameters of convolutional neural networks in the binary classification of mammograms. In this paper, we treat two types of binary classification, namely: classification between normal and abnormal tissues, respectively classification between benign and malignant tumors. In the analysis, we investigate the correlation and impact of batch size and learning rate in increasing the performance of the proposed model. Following the experiments on the MIAS dataset, we concluded that for the treated problems, it is appropriate to choose a learning rate lower than 0.001. For the classification of tissues (normal/abnormal), we obtained the fact that training the model on a batch size of 32 brings the best results, namely an accuracy of 0.67, and for the classification of tumors (benign/malignant), it is more appropriate to use a batch size of 8, for which we obtained an accuracy of 0.63. For the best results configurations, we continued the experiments by investigating the impact of data augmentation. We have increased the number of training data by applying horizontal flip and rotation operations. Following these attempts, we noticed an improvement only for the tissue classification, for which we obtained an accuracy of 0.70.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124919397","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

Building customized Named Entity Recognition models for specific process automation tasks 为特定的流程自动化任务构建定制的命名实体识别模型

2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2022-09-01 DOI: 10.1109/SYNASC57785.2022.00041

Vasile Ionut Iga, G. Silaghi

引用次数: 0