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

{"title":"Deciding Whether two Codes Have the Same Ambiguities is in co-NP","authors":"Yannick Chevalier, M. Rusinowitch","doi":"10.1109/SYNASC57785.2022.00023","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00023","url":null,"abstract":"We define a code to be a finite set of words C on a finite alphabet, and an ambiguity to be an equality between two words in the monoid C*. We recall that a code is uniquely decipherable if its ambiguities are trivial. In this paper we construct a finite-turn deterministic pushdown automaton that recognizes the set of ambiguities of a code. This allows one to show that whether two codes of the same size have the same ambiguities is in co-NP.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"31 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":"124819734","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}

{"title":"Proof of Concept for a Roundtrip Engineering IS for the New Enterprise in the Industry 4.0 Era","authors":"Andrei Chis, Ana-Maria Ghiran, R. Buchmann","doi":"10.1109/SYNASC57785.2022.00040","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00040","url":null,"abstract":"Traditionally, enterprises had relied on information systems (IS) as a medium to represent reality, but recently, there is a growing trend among IS scholars admitting that contemporary environment is calling for a change in the view about the IS' implications in our lives. They acknowledge that our world is increasingly shaped by what is created and performed in various digital environments. In this view, we ask how conceptual modelling (as a particular manifestation of IS) could be used to shape this new environment while continue being used as a means to represent the domain of discourse. This work at hand presents a proof-of-concept for the new enterprise systems where conceptual modelling plays a key role and the modelling tool acts as a transition layer between physical and digital realities.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"26 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":"122345444","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}

{"title":"Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification","authors":"Radu Rosu, Mihaela Breaban, H. Luchian","doi":"10.1109/SYNASC57785.2022.00034","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00034","url":null,"abstract":"Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset. Consequently, data distillation methods are usually tied to a specific ML algorithm. While recent literature deals mainly with distillation of large collections of images in the context of neural network models, tabular data distillation is much less represented and mainly focused on a theoretical perspective. The current paper explores the potential of a simple distillation technique previously proposed in the context of Less-than-one shot learning. The main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy, by integrating optimization steps in the distillation process. The analysis is performed on real-world data sets with various degrees of imbalance. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method, i.e. to generate new data that is able to increase model accuracy when used in conjunction with - as opposed to instead of - the original data.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"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":"128746258","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}

{"title":"AlphaFold-based protein analysis pipeline","authors":"Octavian-Florin Maghiar","doi":"10.1109/SYNASC57785.2022.00061","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00061","url":null,"abstract":"During the 14th 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://github.com/OtimusOne/AFPAP.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"30 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":"128915593","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}

{"title":"Algorithm for intersecting symbolic and approximate linear differential varieties","authors":"S. Deng, Zahra Mohammadi, G. Reid","doi":"10.1109/SYNASC57785.2022.00020","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00020","url":null,"abstract":"This article provides algorithms for systems of approximate linear partial differential equations that exploit exact subsystems. Such exact systems have rational function coefficients over $mathbb{Q}$ and can be reduced to forms (e.g. differential Gröbner bases) by a finite number of differentiations and eliminations using available computer implementations. We will use the rifsimp algorithm in Maple for this purpose. Such algorithms use solvers based on orderings (rankings) of their derivatives, are coordinate dependent, and are prone to instability when applied to approximate input. In contrast, our Geometric Involutive Form algorithm, uses a sequence of geometric differentiations (prolongations) and projections to complete approximate linear systems to geometric involutive form. In particular, it uses numerical linear algebra (especially the SVD) to monitor dimension criteria for termination. However, this latter method can be expensive as the size of the matrices rapidly increases with the number of variables and order of derivatives involved.Approximate differential systems in applications often have exact subsystems and this motivated us to develop the hybrid method described in this article. The first step of the method is to partition the input into an exact subsystem and an approximate subsystem. The exact subsystem is reduced by using our rifsimp algorithm. The reduced exact subsystem is used to simplify the approximate subsystem. The previous partition, reduction and simplification steps are repeated until no new exact equations are found. Then the reduced exact subsystem is used to simplify prolongations of the approximate subsystem. Checking that the jointly prolonged system is geometrically involutive is done by computing dimension criteria of the simplified prolonged approximate system and using the differential Hilbert function of the reduced exact system.Our algorithm is illustrated by determination of approximate symmetry properties of a gravitational potential for a gaseous cloud. It enables a significant reduction of the size of the coefficient matrices of prolongations involved in numerical computations compared to our previous approach. For instance, the dimension of the jet space used for approximate calculations is reduced from dim J7 = 1320 to dim J1 = 20.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"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":"128996067","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}

{"title":"IPO-MAXSAT: The In-Parameter-Order Strategy combined with MaxSAT solving for Covering Array Generation","authors":"Irene Hiess, Ludwig Kampel, Michael Wagner, D. Simos","doi":"10.1109/SYNASC57785.2022.00021","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00021","url":null,"abstract":"Covering arrays (CAs) are combinatorial designs that represent the backbone of combinatorial testing, which is applied most prominently in automated software testing. The generation of optimized CAs is a difficult combinatorial optimization problem being subject to ongoing research. Previous studies have shown that many different algorithmic approaches are best suited for different instances of CAs. In this paper we present the IPO-MAXSAT algorithm, which adopts the prominent inparameter-order (IPO) strategy for CA generation and uses MaxSAT solving to optimize the occurring sub-problems. We devise three different algorithmic variants that use a MaxSAT solver for different sub-problems. These variants are evaluated in an extensive set of experiments where we also consider the usage of different MaxSAT solvers. Further, we provide a comparison against various other algorithms realizing the IPO strategy as well as the state of the art.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"28 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":"115393160","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}

{"title":"The XGBoost Tuning by Improved Firefly Algorithm for Network Intrusion Detection","authors":"Luka Jovanovic, Dijana Jovanovic, Milos Antonijevic, M. Zivkovic, Nebojsa Budimirovic, I. Strumberger, N. Bačanin","doi":"10.1109/SYNASC57785.2022.00050","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00050","url":null,"abstract":"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.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"15 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":"126952791","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}

{"title":"Organic Structures Emerging From Bio-Inspired Graph-Rewriting Automata","authors":"Paul Cousin, A. Maignan","doi":"10.1109/SYNASC57785.2022.00053","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00053","url":null,"abstract":"Graph-Rewriting Automata (GRA) are an extension of Cellular Automata to a dynamic structure using local graph-rewriting rules. This work introduces linear algebra based tools that allow for a practical investigation of their behavior in deeply extended time scales. A natural subset of GRA is explored in different ways thereby demonstrating the benefits of this method. Some elements of the subset were discovered to create chaotic patterns of growth and others to generate organic-looking graph structures. These phenomena suggest a strong relevance of GRA in the modeling natural complex systems. The approach presented here can be easily adapted to a wide range of GRA beyond the chosen subset.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"15 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":"127293910","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}

{"title":"Unsupervised Extractive Summarization with BERT","authors":"A. Dutulescu, M. Dascalu, Stefan Ruseti","doi":"10.1109/SYNASC57785.2022.00032","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00032","url":null,"abstract":"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.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"6 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":"125249622","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}

{"title":"Intelligent Agent for Food Recognition in a Smart Fridge","authors":"Florin Dumitrescu, A. Florea, Mihai Trăscău, Alexandru Sorici","doi":"10.1109/SYNASC57785.2022.00042","DOIUrl":"https://doi.org/10.1109/SYNASC57785.2022.00042","url":null,"abstract":"Respecting an adequate diet is essential for a healthy lifestyle. However, keeping track of the daily consumed food and of what one has in the home fridge may be tedious and time consuming if not backed up by user-friendly applications. In this context, the paper presents some of the main components of an intelligent agent and system aimed to create a user-oriented application for recommending personalized food recipes based on the available ingredients and dietary restrictions in a domestic environment. The developed application includes a component to accurately detect the ingredients from an image taken from inside a fridge equipped with mounted cameras, and allows the user to get information about each ingredient, such as the ingredient type, name and associated calories. To facilitate the development of this application, our own data set of food ingredients was collected and annotated, and an ontology of recipes was developed.","PeriodicalId":446065,"journal":{"name":"2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"10 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":"134602304","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}