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Semi-automatic architectural pattern identification and documentation using architectural primitives

Thomas Haitzer, Uwe Zdun
2015 Journal of Systems and Software  
In this article, we propose an interactive approach for the semi-automatic identification and documentation of architectural patterns based on a domain-specific language.  ...  Secondly, using these annotations, our approach automatically suggests possible pattern instances based on a reusable catalog of patterns and their variants.  ...  [46] propose a visual architecture recovery approach that exploits the package structure of a software system.  ... 
doi:10.1016/j.jss.2014.12.042 fatcat:2pdjbpigo5br7cmrms5hbqkmbi

A machine learning approach against a masked AES

Liran Lerman, Gianluca Bontempi, Olivier Markowitch
2014 Journal of Cryptographic Engineering  
We succeeded to extract each targeted byte of the key of the masked AES with 7.8 traces during the attacking phase with a strategy based solely on machine learning models.  ...  In this paper, we present the first machine learning attack against a specific masking countermeasure (more precisely the lowentropy boolean masking countermeasure of Nassar et al.), using the dataset  ...  Machine learning approach against masking countermeasure We propose a new approach that uses a machine learning approach to: (1) bypass the problem of combining masksrelated information that still keeps  ... 
doi:10.1007/s13389-014-0089-3 fatcat:rsvpfmxfbrgcdcalo4wi3zpd3a

Reservoir Characterization: A Machine Learning Approach [article]

Soumi Chaki
2015 arXiv   pre-print
and also to produce them.It is a difficult problem due to non-linear and heterogeneous subsurface properties and associated with a number of complex tasks such as data fusion, data mining, formulation  ...  Seismic attributes are available over a study area with lower vertical resolution.  ...  Data Partition A common approach in machine learning algorithms is to divide a dataset into training and testing sets for learning and validation, respectively.  ... 
arXiv:1506.05070v2 fatcat:4qaszfl735grfon2bxcs3hy4c4

An efficient high-quality hierarchical clustering algorithm for automatic inference of software architecture from the source code of a software system [article]

Sarge Rogatch
2012 arXiv   pre-print
The architectural diagram shows a tree of subsystems having OOP classes in its leaves (in the other words, a nested software decomposition).  ...  diagram that is reconstructed automatically from the source code of the software system.  ...  architecture recovery approaches discussed in [Maqb2007] .  ... 
arXiv:1202.3335v1 fatcat:tn3hqrqsp5gh3g4oqmagc4er7i

Towards an architectural approach for the dynamic and automatic composition of software components

Antonio Bucchiarone, Andrea Polini, Patrizio Pelliccione, Massimo Tivoli
2006 Proceedings of the ISSTA 2006 workshop on Role of software architecture for testing and analysis - ROSATEA '06  
In this paper we propose a Software Architecture (SA) based approach in which architectural analysis and code synthesis are combined together in order to efficiently and correctly assemble a system out  ...  It is related to the ability to automatically and efficiently (i.e., by reducing the state-explosion phenomenon) synthesize an assembly code for a set of, possibly incompatible, software components.  ...  RELATED WORK The architectural approach to the dynamic and automatic composition of software components presented in this paper is related to a large number of other problems that have been considered  ... 
doi:10.1145/1147249.1147251 dblp:conf/issta/BucchiaronePPT06 fatcat:44hfsmn7gvbbpjp4epkbg2x5na

Geminivirus data warehouse: a database enriched with machine learning approaches

Jose Cleydson F. Silva, Thales F. M. Carvalho, Marcos F. Basso, Michihito Deguchi, Welison A. Pereira, Roberto R. Sobrinho, Pedro M. P. Vidigal, Otávio J. B. Brustolini, Fabyano F. Silva, Maximiller Dal-Bianco, Renildes L. F. Fontes, Anésia A. Santos (+3 others)
2017 BMC Bioinformatics  
Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics  ...  Conclusions: The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain  ...  Funding The authors are grateful to the National Institute of Science and Technology in Plant-Pest Interactions (INCT-IPP), Fundação de Amparo à Pesquisa do estado de Minas Gerais (FAPEMIG), Coordenação  ... 
doi:10.1186/s12859-017-1646-4 pmid:28476106 pmcid:PMC5420152 fatcat:ninpr4l6prbozf7eomhzesdgom

A machine learning approach to online fault classification in HPC systems

Alessio Netti, Zeynep Kiziltan, Ozalp Babaoglu, Alina Sîrbu, Andrea Bartolini, Andrea Borghesi
2020 Future generations computer systems  
In this paper, we propose a fault classification method for HPC systems based on machine learning.  ...  In order to train and evaluate our machine learning classifiers, we inject faults to an in-house experimental HPC system using FINJ, and generate a fault dataset which we describe extensively.  ...  Acknowledgements A. Netti has been supported by the EU project Oprecomp-Open Transprecision Computing (grant agreement 732631). A.  ... 
doi:10.1016/j.future.2019.11.029 fatcat:mljjxgvq2naj7dbi2lnsndngfm

A semi-automated machine learning-aided approach to quantitative analysis of centrosomes and microtubule organization [article]

Divya Ganapathi Sankaran, Bharath Hariharan, Chad G Pearson
2020 bioRxiv   pre-print
We developed a quantitative image-processing and machine learning-aided approach for the automated analysis of MT organization.  ...  We designed a convolutional neural network-based approach for detecting centrosomes and an automated pipeline for analyzing MT organization around centrosomes, encapsulated in a semi-automatic graphical  ...  BH designed the algorithms, the machine learning models and the graphical interface. BH and CGP supervised the project and wrote the manuscript.  ... 
doi:10.1101/2020.01.03.894071 fatcat:jtfppj3z2vbf3got3non7m5pfu

From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

Valeria Croce, Gabriella Caroti, Livio De Luca, Kévin Jacquot, Andrea Piemonte, Philippe Véron
2021 Remote Sensing  
This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques.  ...  In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric  ...  In tackling this issue, the proposed approach is based on: (i) A semantic segmentation via machine learning: An ML model is trained in order to automatically structure and classify 3D survey data.  ... 
doi:10.3390/rs13030461 fatcat:aq2g6lh265gcxec5dt7ir575hq

A Machine Learning Approach to Reduce Dimensional Space in Large Datasets

Rafael Munoz Terol, Alejandro Reina Reina, Saber Ziaei, David Gil
2020 IEEE Access  
For this purpose, our contribution in this paper is a novel architecture that we have divided into five phases presenting a hybrid method of machine learning to reduce dimensional space in large datasets  ...  It does this by applying a more strong and complicated architecture to learn regression and classification models for complex datasets.  ... 
doi:10.1109/access.2020.3012836 fatcat:6l47tyauffa3riv3eheo4vmqw4

A machine learning approach to TCP state monitoring from passive measurements

Desta Haileselassie Hagos, Paal E. Engelstad, Anis Yazidi, Oivind Kure
2018 2018 Wireless Days (WD)  
We would like to thank the Research Infrastructure Services Group at the University of Oslo for the use of multicore cluster machines.  ...  We would like to thank the Research Infrastructure Services Group at the University of Oslo for the use of multicore cluster machines. Acknowledgements.  ...  better approach to estimate the cwnd and how fast the recovery is.  ... 
doi:10.1109/wd.2018.8361713 dblp:conf/wd/HagosEYK18 fatcat:hmeh7nfa7bbwxnncel5xnyytim

Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus [article]

Haruna Chiroma, Absalom E Ezugwu, Fatsuma Jauro, Mohammed A Al-Garadi, Idris N Abdullahi, Liyana Shuib
2020 medRxiv   pre-print
Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective.  ...  Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed.  ...  . ; https://doi.org/10.1101/2020.11.04.20225698 doi: medRxiv preprint Tiwari et al. (2020) used a machine learning approach to predict the COVID-19 pandemic number of cases, recoveries, and deaths in  ... 
doi:10.1101/2020.11.04.20225698 fatcat:bb2pdhsanfe5bl57p2phnhys2q

A Machine Learning Approach for Secure Intrusion Detection in Wireless Sensor Networks

Arun Kumar Silicery
2017 International Journal for Research in Applied Science and Engineering Technology  
By applying feature selection approach in machine learning in, the NSLKDD data collection gained can be decreased and furthermore can enhance the Intrusion detection utilizing the captured information.  ...  By machine learning procedures, we can build number of new unknown attacks the network of Intrusion detection can be implemented.  ...  Machine Learning Approach 1) Neural Network Approach: In the neural system approach to deal with intrusion detection, the neural system figures out how to predict the nature of the different clients and  ... 
doi:10.22214/ijraset.2017.11365 fatcat:acs4rxzqmbfstak5gw6idyqcme

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam
2019 Machine Learning  
Learning approaches use training data to learn how to match records from different sources. ey include probabilistic approaches and supervised machine learning techniques. 2.  ...  To this end, machine learning algorithms can handle categorical data as well.  ...  In the remainder of this chapter, we highlight key supervised learning approaches and their applications in the field of bioinformatics. ere are several supervised learning strategies in existence, and  ... 
doi:10.1007/s10994-019-05810-5 fatcat:nulmjvxvwjgojfoe2ywv3pjrpu

An approach to forecast impact of Covid‐19 using supervised machine learning model

Senthilkumar Mohan, John A, Ahed Abugabah, Adimoolam M, Shubham Kumar Singh, Ali kashif Bashir, Louis Sanzogni
2021 Software, Practice & Experience  
In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed.  ...  K E Y W O R D S Covid-19, ensemble learning, healthcare, machine learning, prediction Softw: Pract Exper. 2021;1-17. wileyonlinelibrary.com/journal/spe  ...  The authors Alimadadi et al., 3 developed a text and data mining technique to predict Covid-19 parameters and this work was analyzed with the help of a machine learning method for predicting spread,  ... 
doi:10.1002/spe.2969 fatcat:o3llmbmrjfa2zpze5tcvjhwoce
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