Pub Date : 2021-09-13DOI: 10.18690/978-961-286-516-0.6
Rok Kukovec, Špela Pečnik, Iztok Fister Jr., S. Karakatič
The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of these algorithms can be compromised with adversarial perturbations of images. This is where images are seemingly modified imperceptibly, such that humans see little to no di˙erence, but the neural network classifies t he m otif i ncorrectly. This paper explores the adversarial image modifica-tion with an evolutionary algorithm, so that the AlexNet convolutional neural network cannot recognize previously clear motifs while preserving the human perceptibility of the image. The ex-periment was implemented in Python and tested on the ILSVRC dataset. Original images and their recreated counterparts were compared and contrasted using visual assessment and statistical metrics. The findings s uggest t hat t he human eye, without prior knowledge, will hardly spot the di˙erence compared to the original images.
{"title":"Adversarial Image Perturbation with a Genetic Algorithm","authors":"Rok Kukovec, Špela Pečnik, Iztok Fister Jr., S. Karakatič","doi":"10.18690/978-961-286-516-0.6","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.6","url":null,"abstract":"The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of these algorithms can be compromised with adversarial perturbations of images. This is where images are seemingly modified imperceptibly, such that humans see little to no di˙erence, but the neural network classifies t he m otif i ncorrectly. This paper explores the adversarial image modifica-tion with an evolutionary algorithm, so that the AlexNet convolutional neural network cannot recognize previously clear motifs while preserving the human perceptibility of the image. The ex-periment was implemented in Python and tested on the ILSVRC dataset. Original images and their recreated counterparts were compared and contrasted using visual assessment and statistical metrics. The findings s uggest t hat t he human eye, without prior knowledge, will hardly spot the di˙erence compared to the original images.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150446","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.13
Ðorže Klisura
In this paper, we propose a convention for repre-senting non-planar graphs and their least-crossing embeddings in a canonical way. We achieve this by using state-of-the-art tools such as canonical labelling of graphs, Nauty’s Graph6 string and combinatorial representations for planar graphs. To the best of our knowledge, this has not been done before. Besides, we implement the men-tioned procedure in a SageMath language and compute embeddings for certain classes of cubic, vertex-transitive and general graphs. Our main contribution is an extension of one of the graph data sets hosted on MathDataHub, and towards extending the SageMath codebase.
{"title":"Embedding Non-planar Graphs: Storage and Representation","authors":"Ðorže Klisura","doi":"10.18690/978-961-286-516-0.13","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.13","url":null,"abstract":"In this paper, we propose a convention for repre-senting non-planar graphs and their least-crossing embeddings in a canonical way. We achieve this by using state-of-the-art tools such as canonical labelling of graphs, Nauty’s Graph6 string and combinatorial representations for planar graphs. To the best of our knowledge, this has not been done before. Besides, we implement the men-tioned procedure in a SageMath language and compute embeddings for certain classes of cubic, vertex-transitive and general graphs. Our main contribution is an extension of one of the graph data sets hosted on MathDataHub, and towards extending the SageMath codebase.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127417276","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.4
Martin Domajnko, Nikola Glavina, Aljaž Žel
This paper explores the challenges and devised solutions for embedded development which arose during the COVID-19 pandemic. While software development, nowadays with modern tools and services such as git, virtual machines and commu-nication suits, is relatively una˙ected by resource location. That is not the case for firmware and embedded systems, which relies on physical hard-ware for design, development, and testing. To overcome the limitations of remote work and ob-structed access to actual hardware, two ideas were implemented and tested. First, based on inte-grated circuit emulation using QEMU to emulate an ARM core and custom software to facilitate communication with the embedded system. Sec-ond, remote programming and debugging over the internet with a dedicated computer system acting as a middle man between a development environ-ment and physical hardware using OpenOCD de-bugger.
{"title":"System for Remote Collaborative Embedded Development","authors":"Martin Domajnko, Nikola Glavina, Aljaž Žel","doi":"10.18690/978-961-286-516-0.4","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.4","url":null,"abstract":"This paper explores the challenges and devised solutions for embedded development which arose during the COVID-19 pandemic. While software development, nowadays with modern tools and services such as git, virtual machines and commu-nication suits, is relatively una˙ected by resource location. That is not the case for firmware and embedded systems, which relies on physical hard-ware for design, development, and testing. To overcome the limitations of remote work and ob-structed access to actual hardware, two ideas were implemented and tested. First, based on inte-grated circuit emulation using QEMU to emulate an ARM core and custom software to facilitate communication with the embedded system. Sec-ond, remote programming and debugging over the internet with a dedicated computer system acting as a middle man between a development environ-ment and physical hardware using OpenOCD de-bugger.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271030","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.12
Ivan Kovačič, David Bajs, M. Ojsteršek
This paper describes the methodology of data preparation and analysis of the text similarity required for plagiarism detection on the CORE data set. Firstly, we used the CrossREF API and Microsoft Academic Graph data set for metadata enrichment and elimination of duplicates of doc-uments from the CORE 2018 data set. In the second step, we used 4-gram sequences of words from every document and transformed them into SHA-256 hash values. Features retrieved using hashing algorithm are compared, and the result is a list of documents and the percentages of cov-erage between pairs of documents features. In the third step, called pairwise feature-based ex-haustive analysis, pairs of documents are checked using the longest common substring.
{"title":"Methodology for the Assessment of the Text Similarity of Documents in the CORE Open Access Data Set of Scholarly Documents","authors":"Ivan Kovačič, David Bajs, M. Ojsteršek","doi":"10.18690/978-961-286-516-0.12","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.12","url":null,"abstract":"This paper describes the methodology of data preparation and analysis of the text similarity required for plagiarism detection on the CORE data set. Firstly, we used the CrossREF API and Microsoft Academic Graph data set for metadata enrichment and elimination of duplicates of doc-uments from the CORE 2018 data set. In the second step, we used 4-gram sequences of words from every document and transformed them into SHA-256 hash values. Features retrieved using hashing algorithm are compared, and the result is a list of documents and the percentages of cov-erage between pairs of documents features. In the third step, called pairwise feature-based ex-haustive analysis, pairs of documents are checked using the longest common substring.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126233596","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.11
Luka Lukač
Nowadays, sport data analysis is one of the cru-cial factors, used to enhance the athletes’ per-formance, which can depend upon many di˙er-ent circumstances. One of those is the area of an exercise, which can dramatically impact on an athlete’s performance. Since not enough devotion has been given to this topic, this study focuses on extracting and analysing parts of exercises, which take place inside of a specific area, using principles from another part of Computer Science, Compu-tational Geometry.
{"title":"Extraction and Analysis of Sport Activity Data Inside Certain Area","authors":"Luka Lukač","doi":"10.18690/978-961-286-516-0.11","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.11","url":null,"abstract":"Nowadays, sport data analysis is one of the cru-cial factors, used to enhance the athletes’ per-formance, which can depend upon many di˙er-ent circumstances. One of those is the area of an exercise, which can dramatically impact on an athlete’s performance. Since not enough devotion has been given to this topic, this study focuses on extracting and analysing parts of exercises, which take place inside of a specific area, using principles from another part of Computer Science, Compu-tational Geometry.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126458593","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.15
Nina Murks, Anže Omerzu, Borko Bošković
V £lanku smo predstavili pristop k analizi sen-timenta komentarjev hotelskih gostov s pomo£jo slovarjev in metode Naivni Bayes. Najprej smo zgradili slovarja sentimenta, ki sta vsebovala n-grame, ter njihove verjetnosti, da pripadajo pozi-tivnemu ali negativnemu razredu. Nato smo s po-mo£jo zgrajenih slovarjev klasificirali komentarje hotelov, pri £emer smo uporabili metodo Naivni Bayes. Pri klasifikaciji komentarjev s mo ra£u-nali klasifikacijske vrednosti o z. verjetnosti, da so posamezni komentarji pozitivni ali negativni. Komentarje smo klasificirali s p omo£jo unigra-mov in bigramov, ter rezultate primerjali z re-zultati iz literature. Pri unigramih smo dosegli natan£nost 0,92, pri bigramih je natan£nost zna-šala 0,80. Klasifikacijske v rednosti posameznih komentarjev smo si shranili, pri £emer smo pri komentarjih, ki smo jih klacificirali kot negativne, dodali negativen predznak. Predzna£ene klasifi-kacijske vrednosti smo nato sešteli, za vsak hotel ter na tak na£in izra£unali hotelom pripadajo£e to£ke. To£ke hotelov so v našem primeru poka-zatelj splošnega zadovoljstva hotelskih gostov, ki ga najdemo v komentarjih. Glede na to£ke smo hotele uredili po vrsti in prišli do lestvice hote-lov, pri katerih najdemo najbolj pozitivne komen-tarje.
{"title":"Analiza sentimenta komentarjev hotelov z uporabo slovarjev in metode Naivni Bayes","authors":"Nina Murks, Anže Omerzu, Borko Bošković","doi":"10.18690/978-961-286-516-0.15","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.15","url":null,"abstract":"V £lanku smo predstavili pristop k analizi sen-timenta komentarjev hotelskih gostov s pomo£jo slovarjev in metode Naivni Bayes. Najprej smo zgradili slovarja sentimenta, ki sta vsebovala n-grame, ter njihove verjetnosti, da pripadajo pozi-tivnemu ali negativnemu razredu. Nato smo s po-mo£jo zgrajenih slovarjev klasificirali komentarje hotelov, pri £emer smo uporabili metodo Naivni Bayes. Pri klasifikaciji komentarjev s mo ra£u-nali klasifikacijske vrednosti o z. verjetnosti, da so posamezni komentarji pozitivni ali negativni. Komentarje smo klasificirali s p omo£jo unigra-mov in bigramov, ter rezultate primerjali z re-zultati iz literature. Pri unigramih smo dosegli natan£nost 0,92, pri bigramih je natan£nost zna-šala 0,80. Klasifikacijske v rednosti posameznih komentarjev smo si shranili, pri £emer smo pri komentarjih, ki smo jih klacificirali kot negativne, dodali negativen predznak. Predzna£ene klasifi-kacijske vrednosti smo nato sešteli, za vsak hotel ter na tak na£in izra£unali hotelom pripadajo£e to£ke. To£ke hotelov so v našem primeru poka-zatelj splošnega zadovoljstva hotelskih gostov, ki ga najdemo v komentarjih. Glede na to£ke smo hotele uredili po vrsti in prišli do lestvice hote-lov, pri katerih najdemo najbolj pozitivne komen-tarje.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133192729","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.9
Tjaša Heričko, Boštjan Šumak, Saša Brdnik
Web performance testing with tools such as Google Lighthouse is a common task in software practice and research. However, variability in time-based performance measurement results is observed quickly when using the tool, even if the website has not changed. This can occur due to variability in the network, web, and client devices. In this paper, we investigated how this challenge was addressed in the existing literature. Furthermore, an experiment was conducted, highlighting how unrepresentative measurements can result from single runs; thus, researchers and practitioners are advised to run performance tests multiple times and use an aggregation value. Based on the empirical results, 5 consecutive runs using a median to aggregate results reduce variability greatly, and can be performed in a reasonable time. The study’s findings alert to p otential pitfalls when using single run-based measurement results and serve as guidelines for future use of the tool.
{"title":"Towards Representative Web Performance Measurements with Google Lighthouse","authors":"Tjaša Heričko, Boštjan Šumak, Saša Brdnik","doi":"10.18690/978-961-286-516-0.9","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.9","url":null,"abstract":"Web performance testing with tools such as Google Lighthouse is a common task in software practice and research. However, variability in time-based performance measurement results is observed quickly when using the tool, even if the website has not changed. This can occur due to variability in the network, web, and client devices. In this paper, we investigated how this challenge was addressed in the existing literature. Furthermore, an experiment was conducted, highlighting how unrepresentative measurements can result from single runs; thus, researchers and practitioners are advised to run performance tests multiple times and use an aggregation value. Based on the empirical results, 5 consecutive runs using a median to aggregate results reduce variability greatly, and can be performed in a reasonable time. The study’s findings alert to p otential pitfalls when using single run-based measurement results and serve as guidelines for future use of the tool.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129044543","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.14
Nina Velikajne, Miha Moškon
Analiza ritmi£nosti števnih podatkov je postala pomembna v mnogih vidikih znanosti, inženirstva in celo ekonomije. Obstajajo metode z namenom detekcije ritmi£nosti zveznih podatkov, ki pa ve£i-noma niso primerne za analizo števnih podatkov. V prispevku predstavimo metodologijo, ki omo-go£a analizo ritmi£nosti v števnih podatkih. Me-toda združuje metodo cosinor z uporabo razli£-nih ra£unskih regresijskih modelov, ki so primerni za analizo števnih podatkov. Omogo£a tako de-tekcijo ritma kot tudi ocenitev parametrov ritma, primerjavo zgrajenih modelov in iskanje optimal-nega števila komponent za metodo cosinor ter is-kanje najbolj ustreznega tipa števnega modela. Vzpostavljena metoda omogo£a primerjavo zazna-nega ritma v odvisnosti od razli£nih parametrov ritmi£nosti in izra£un njihovih intervalov zaupa-nja. Celotno metodologijo smo testirali na te-denski periodi£nosti realnih podatkov COVID-19 obolenj v Sloveniji.
{"title":"Analiza ritmičnosti števnih podatkov z uporabo modela cosinor","authors":"Nina Velikajne, Miha Moškon","doi":"10.18690/978-961-286-516-0.14","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.14","url":null,"abstract":"Analiza ritmi£nosti števnih podatkov je postala pomembna v mnogih vidikih znanosti, inženirstva in celo ekonomije. Obstajajo metode z namenom detekcije ritmi£nosti zveznih podatkov, ki pa ve£i-noma niso primerne za analizo števnih podatkov. V prispevku predstavimo metodologijo, ki omo-go£a analizo ritmi£nosti v števnih podatkih. Me-toda združuje metodo cosinor z uporabo razli£-nih ra£unskih regresijskih modelov, ki so primerni za analizo števnih podatkov. Omogo£a tako de-tekcijo ritma kot tudi ocenitev parametrov ritma, primerjavo zgrajenih modelov in iskanje optimal-nega števila komponent za metodo cosinor ter is-kanje najbolj ustreznega tipa števnega modela. Vzpostavljena metoda omogo£a primerjavo zazna-nega ritma v odvisnosti od razli£nih parametrov ritmi£nosti in izra£un njihovih intervalov zaupa-nja. Celotno metodologijo smo testirali na te-denski periodi£nosti realnih podatkov COVID-19 obolenj v Sloveniji.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457999","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.8
Vid Keršič
Artificial intelligence and its subfields have be-come part of our everyday lives and eÿciently solve many problems that are very hard for us humans. But in some tasks, these methods strug-gle, while we, humans, are much better solvers with our intuition. Because of that, the ques-tion arises: why not combine intelligent methods with human skills and intuition? This paper pro-poses an Interactive Evolutionary Computation approach to the Permutation Flow Shop Schedul-ing Problem by incorporating human-in-the-loop in MAX-MIN Ant System through gamification of the problem. The analysis shows that combin-ing the evolutionary computation approach and human-in-the-loop leads to better solutions, sig-nificantly when the complexity of the problem in-creases.
{"title":"Interactive Evolutionary Computation Approach to Permutation Flow Shop Scheduling Problem","authors":"Vid Keršič","doi":"10.18690/978-961-286-516-0.8","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.8","url":null,"abstract":"Artificial intelligence and its subfields have be-come part of our everyday lives and eÿciently solve many problems that are very hard for us humans. But in some tasks, these methods strug-gle, while we, humans, are much better solvers with our intuition. Because of that, the ques-tion arises: why not combine intelligent methods with human skills and intuition? This paper pro-poses an Interactive Evolutionary Computation approach to the Permutation Flow Shop Schedul-ing Problem by incorporating human-in-the-loop in MAX-MIN Ant System through gamification of the problem. The analysis shows that combin-ing the evolutionary computation approach and human-in-the-loop leads to better solutions, sig-nificantly when the complexity of the problem in-creases.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123339894","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 : 2021-09-13DOI: 10.18690/978-961-286-516-0.1
Aljaž Frančič, A. Holobar, M. Zorman
{"title":"On Artefact Elimination in High Density Electromyograms by Independent Component Analysis","authors":"Aljaž Frančič, A. Holobar, M. Zorman","doi":"10.18690/978-961-286-516-0.1","DOIUrl":"https://doi.org/10.18690/978-961-286-516-0.1","url":null,"abstract":"<jats:p />","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126350389","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}
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