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Authors and Collaborator Groups Ranking Analysis on SYNASC using Centrality Measures

Authors and Collaborator Groups Ranking Analysis on SYNASC u...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Victor Bogdan Alin Brînduşescu West University of Timişoara Timişoara Romania

the constant increase of published papers and participating authors in conferences has risen the interest in studies related to conference and co-authorship analysis techniques. there are numerous different studies in the literature, which offer distinct perspectives regarding the author ranking, impact, collaborations and research interests through various analysis techniques. In our research, we are introducing a new collaborator group ranking analysis based on four different centrality measures, using published papers data from the SYNASC conference during the 2005-2021 period, with which we construct dynamic co-authorship networks. By using the proposed approach, we aim to study collaboration properties at micro-level, which could also determine the impact of the authors and collaborator groups on the conference itself. the results which we obtained are highlighting that some of the authors and collaborator groups excelled in multiple ranking types, by having a constant top ranking, thus also suggesting a significant overall impact, developed over time, on the conference.

关键词： Weight measurement Productivity Correlation scientific computing Collaboration Time measurement Frequency diversity

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Cerebral Metastases Segmentation using Transfer Gliomas Learning and GrabCut

Cerebral Metastases Segmentation using Transfer Gliomas Lear...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Ciprian-Mihai Ceauşescu Bogdan Alexe University of Bucharest Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy University of Bucharest

Segmentation of medical images is an important area of research that can be used in prognosis prediction and patient treatment. Due to the high variability of data, the task to develop an accurate segmentation method remains challenging. In this paper we address the problem of cerebral metastases segmentation and focus our analysis on the BrainMetShare dataset. In order to enhance the metastases segmentation performance we propose a two stage method. In the first stage we employ a transfer learning procedure where we train an Unet model on the similar task of low and high grade gliomas segmentation provided by the BraTS dataset and then fine-tune the model for solving our problem of cerebral metastases segmentation. In the second stage we use GrabCut to refine the metastases segmentation masks obtained from the first stage. In the experimental evaluation we show that our two stage method based on transfer learning and GrabCut progressively outperforms the baseline model trained only on cerebral metastases data from BrainMetShare.

关键词： Image segmentation scientific computing Computational modeling Transfer learning Medical treatment Brain modeling Data models

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Application of symbolic Regression in Polymer Processing

Application of Symbolic Regression in Polymer Processing

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Wolfgang Roland Michael Kommenda Gerald R. Berger-Weber Institute of Polymer Processing and Digital Transformation Johannes Kepler University Linz Linz Austria Josef Ressel Centre for Symbolic Regression University of Applied Sciences Upper Austria Hagenberg Austria

Modeling and simulation is essential in polymer processing for predicting process characteristics and designing processing machines. Traditional models are based on analytical approaches. Over the last decades numerical simulation techniques have grown significantly with the rising computational power. With the ongoing digitalization the available data increased significantly and data-based modeling techniques have become popular also for production systems. Utilizing the available data powerful models, for instance, decision trees and artificial neural networks, can be trained. the prediction accuracy is strongly governed by the quality of the underlying training data. In this work, a hybrid approach is presented combining analytical, numerical and data-based approaches efficiently to overcome the limitations of the individual techniques. As a result, explicit symbolic regression models are obtained, which are optimized on the basis of a numerically derived dataset. the power of this approach is demonstrated by a selected use-case. these highly accurate models may be implemented into any further application.

关键词： Training Analytical models Solid modeling Computational modeling Training data Predictive models Mathematical models

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AlphaFold-based protein analysis pipeline

AlphaFold-based protein analysis pipeline

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Octavian-Florin Maghiar Faculty of Mathematics and Computer Science West University of Timisoara Timişoara Romania

During the 14 th edition of the Critical Assessment of protein Structure Prediction competition, great progress towards solving the protein structure prediction problem has been achieved by the winning model, DeepMind's AlphaFold2. thanks to AlphaFold2's significant leap in accuracy, new possibilities in protein structure analysis and design have been opened. this paper presents a new protein analysis pipeline that builds upon the predictions of AlphaFold2. the core functionality of the pipeline is to determine and present different properties based on the protein sequence and the predicted three-dimensional structure. Some of the available features include computing physicochemical properties, executing an evolutionary analysis by aligning the sequence against databases such as Pfam and Swiss-Prot/UniRef90, the detection of binding pockets using P2Rank, and the molecular docking of ligands using AutoDock Vina. the results produced by the pipeline can be visualized as a MultiQC HTML report. the performance of the pipeline has been analyzed using a small dataset of protein structures, and the developed workflow has then been used to compare the accuracy of AlphaFold2's predictions against other experimental structures. the pipeline has been developed using Nextflow, a popular workflow manager for bioinformatic analyses, and has been made freely available at https://***/OtimusOne/AFPAP.

关键词： Proteins Visualization scientific computing Pipelines Predictive models Prediction algorithms Protein sequence

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Pedestrian Trajectory Prediction Based on Tree Method using Graph Neural Networks

Pedestrian Trajectory Prediction Based on Tree Method using ...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Bogdan Ilie Sighencea Department of Applied Electronics Politehnica University of Timișoara Timișoara Romania

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.

关键词： Measurement scientific computing Predictive models Video surveillance Prediction algorithms Graph neural networks Trajectory

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the Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms

The Impact of Convolutional Neural Network Parameters in the...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Mădălina Dicu Laura Dioşan Anca Andreica Camelia Chira Alin Cordoş Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj-Napoca Romania PIXELDATA Cluj-Napoca Romania

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.

关键词： Training Correlation scientific computing Malignant tumors Neural networks Training data Mammography

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An Evaluation of Image Texture Descriptors and their Invariant Properties

An Evaluation of Image Texture Descriptors and their Invaria...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Roxana Sipos-Lascu Laura-Silvia Dioşan Department of Computer Science Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj-Napoca Romania

Image processing applications include image classification, image segmentation, image synthesis and many others. Each such task depends on extracting an effective set of features to characterize the images, and texture analysis has proven to output some of the most valuable features. For this reason, image texture analysis has been an actively researched topic and numerous methods have been proposed, each of them having its advantages and limitations. In practical applications, it is impossible to ensure that all images have the same scale, rotation, viewpoint, etc., so texture analysis methods should ideally be invariant. this study inspects the most commonly used operators for extracting textural features, tests their accuracy in classifying the Kylberg texture dataset, and evaluates their invariant properties by the means of various synthetically transformed images. By conducting this analysis, we identified the shortcomings of the existing approaches, and will be able to address them in our future work by formulating some improvements to existing operators to increase their accuracy and to make them invariant to a larger set of transformations.

关键词： Image texture Image segmentation scientific computing Image synthesis Feature extraction Task analysis Image texture analysis

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the XGBoost Tuning by Improved Firefly Algorithm for Network Intrusion Detection

The XGBoost Tuning by Improved Firefly Algorithm for Network...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Luka Jovanovic Dijana Jovanovic Milos Antonijevic Miodrag Zivkovic Nebojsa Budimirovic Ivana Strumberger Nebojsa Bacanin Technical Faculty Singidunum University Belgrade Serbia College of Academic Studies "Dositej" Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

Research proposed in this article presents a novel improved version of widely adopted firefly algorithm and its application for tuning the eXtreme Gradient Boosting (XGboost) hyper-parameters for network intrusion detection. One of the greatest issues from the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGboost optimized with enhanced firefly algorithm, this challenge is addressed. Devised method was adopted and tested against recent benchmarking USNW-NB15 dataset for network intrusion detection. Achieved results of proposed method were compared to the ones obtained by standard machine learning methods, as well as to XGBoost models tuned by other swarm algorithms. Reported comparative analysis results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimization challenge and that it can be used for improving classification accuracy, precision, recall, f1-score and area under the receiver operating characteristic curve for network intrusion detection datasets.

关键词： Measurement Support vector machines Machine learning algorithms Metaheuristics Network intrusion detection Feature extraction Classification algorithms

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Fully-adaptive Model for Broadcasting with Universal Lists

Fully-adaptive Model for Broadcasting with Universal Lists

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Saber Gholami Hovhannes A. Harutyunyan Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada

In classical broadcasting, a piece of information must be transmitted to all entities of a network as quickly as possible, starting from a particular member. Since this problem has an enormous number of applications and is proven to be NP-Hard, several models are defined in the literature while trying to simulate real-world situations and relax several constraints. A well-known branch of broadcasting utilizes a universal list throughout the process. that is, once a vertex is informed, it must follow its corresponding list, regardless of the originator and the neighbor it received the message. the problem of broadcasting with universal lists could be categorized into two sub-models: non-adaptive and adaptive. In the latter model, a sender will skip the vertices on its list from which it has received the message, while those vertices will not be skipped in the first *** this study, we will present another sub-model called fully adaptive. Not only does this model benefit from a significantly better space complexity compared to the classical model, but, as will be proved, it is faster than the two other sub-models. Since the suggested model fits real-world network architectures, we will design optimal broadcast algorithms for well-known interconnection networks such as trees, grids, and cube-connected cycles under the fully-adaptive model. We also present a tight upper bound for tori under the same model.

关键词： Adaptation models Upper bound scientific computing Multiprocessor interconnection Computational modeling Broadcasting Network architecture

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Unsupervised Extractive Summarization with BERT

Unsupervised Extractive Summarization with BERT

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Andreea - Nicoleta Dutulescu Mihai Dascalu Stefan Ruseti Computer Science Department University Politehnica of Bucharest Bucharest Romania

the task of document summarization became more pressing as the information volume increased exponentially from news websites to scientific writings. As such, the necessity for tools that automatically summarize written text, while keeping its meaning and extracting relevant information, increased. Extractive summarization is an NLP task that targets the identification of relevant sentences from a document and the creation of a summary with those phrases. While extensive research and large datasets are available in English, Romanian and other low-resource languages lack such methods and corpora. In this work, we introduce a new approach for summarization using a Masked Language Model for assessing sentence importance, and we research several baselines for the Romanian language including K-Means with BERT embeddings, an MLP considering handcrafted features, and PacSum. In addition, we also present an evaluation corpus to be used for assessing current and future models. the unsupervised methods do not require large datasets for training and make use of low computational power. All of the proposed approaches consider BERT, a state-of-the-art Transformer used for generating contextualized embeddings. the obtained ROUGE score of 56.29 is comparable with state-of-the-art scores and the METEOR average of 51.20 supersedes the most advanced current model.

关键词： Training scientific computing Computational modeling Bit error rate Pressing Writing Transformers

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