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8. Advancing Research Reproducibility in Machine Learning through Blockchain Technology.

Author

Filatovas, Ernestas, Stripinis, Linas, Orts, Francisco, and Paulavičius, Remigijus

Subjects
*MACHINE learning, *BLOCKCHAINS, *REPRODUCIBLE research, *ARTIFICIAL intelligence, *ACCESS control, *RESEARCH personnel
Abstract

Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence. [ABSTRACT FROM AUTHOR]

Published
2024
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10. A Decision Support System for Solving Multiple Criteria Optimization Problems

Author

Filatovas, Ernestas and Kurasova, Olga

Abstract

In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria and solving single-criterion optimization problems (SOPs). However, the WS approach does not guarantee either finding evenly distributed solutions of a Pareto front, or finding Pareto optimal solutions in non-convex regions. In order to solve the problems, some methods have been proposed. In this paper, an adaptive weighted-sum (AWS) approach has been used. We suggest presenting the solutions, obtained not only by the weighted-sum method, but also by the adaptive weighted-sum approach, to a decision maker (DM). Thus the decision making will be facilitated.

Published
2011

12. Quality Prediction of Compressed Images via Classification

Author

Tichonov, Jevgenij, Kurasova, Olga, Filatovas, Ernestas, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2017
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17. AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery.

Author

Gudzius, Povilas, Kurasova, Olga, Darulis, Vytenis, and Filatovas, Ernestas

Subjects
REMOTE-sensing images, OBJECT recognition (Computer vision), REMOTE sensing, ARTIFICIAL satellite launching
Abstract

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field. [ABSTRACT FROM AUTHOR]

Published
2023
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36. A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search.

Author

Filatovas, Ernestas, Lančinskas, Algirdas, Kurasova, Olga, and Žilinskas, Julius

Subjects
EVOLUTIONARY algorithms, DECISION making, STOCHASTIC processes, MATHEMATICAL optimization, APPROXIMATION theory
Abstract

Incorporation of a decision maker's preferences into multi-objective evolutionary algorithms has become a relevant trend during the last decade, and several preference-based evolutionary algorithms have been proposed in the literature. Our research is focused on improvement of a well-known preference-based evolutionary algorithm R-NSGA-II by incorporating a local search strategy based on a single agent stochastic approach. The proposed memetic algorithm has been experimentally evaluated by solving a set of well-known multi-objective optimization benchmark problems. It has been experimentally shown that incorporation of the local search strategy has a positive impact to the quality of the algorithm in the sense of the precision and distribution evenness of approximation. [ABSTRACT FROM AUTHOR]

Published
2017
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37. Daugiakriterinių optimizavimo uždavinių sprendimas interaktyviuoju būdu

Author

Filatovas, Ernestas, Kurasova, Olga, Dzemyda, Gintautas, Bareiša, Eduardas, Baronas, Romas, Čenys, Antanas, Gudas, Saulius, Mockus, Jonas, Navakauskas, Dalius, and Vilnius University

Subjects
Informatics Engineering, Daugiakriterinis optimizavimas, Sprendimų paramos sistemos, Multiple Criteria Optimization, Decision Making, Decision Support Systems, Parallel Computing, Lygiagretus skaičiavimai, Sprendimų priėmimas
Abstract

In practice, optimization problems are often multiple criteria. The criteria are usually contradictory, so the final decision depends on a decision maker. When the problem is solved interactively, the decision maker can change his/her preferences in decision process. Moreover, it is important to obtain solutions from the whole Pareto front. A decision support system adapted to the specific of the problem is essential for solving multiple criteria optimization problems interactively. The objects of research are multiple criteria optimization problems, interactive methods for solving these problems, interactive decision support systems, and application of parallel computing in decision support systems. Multiple criteria optimization methods are analyzed in the dissertation. The focus of attention is the methods for a uniform distribution of solutions on the Pareto front as well as the interactive methods. An interactive way for solving multicriteria optimization problems, which finds alternative solutions uniformly distributed on the Pareto front is proposed and investigated in this dissertation. An interactive decision support system which integrates the created interactive solving way, the decision process visualization and parallelization for multiple criteria optimization is developed. The solving strategies, when a multiple criteria optimization problem is solved interactively, using a computer cluster are developed and compared experimentally. The time required for a... [to full text] Praktikoje dažnai tenka spręsti sudėtingus daugiakriterinius optimizavimo uždavinius, kai kriterijai būna prieštaringi, o galutinis apsisprendimas priklauso nuo sprendimų priėmėjo. Kai sprendimų priėmėjas dalyvauja sprendimo procese interaktyviai, tai jis gali koreguoti prioritetus ir siekiamus tikslus uždavinio sprendimo eigoje, kas įgalina spęsti uždavinius, turinčius daug kriterijų ir apribojimų. Be to, sprendimo priėmėjui svarbu gauti sprendinius iš visos Pareto aibės. Interaktyviam uždavinių sprendimui būtina sprendimų paramos sistema, kurios grafinė sąsaja yra pritaikyta sprendžiamam uždaviniui. Šio darbo tyrimų sritis yra interaktyvus daugiakriterinių optimizavimo uždavinių sprendimas bei sprendimų paramos sistemos. Disertacijoje nagrinėjant daugiakriterinio optimizavimo metodus, didesnis dėmesys skirtas metodams, užtikrinantiems gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje bei interaktyviems metodams. Pasiūlytas ir ištirtas daugiakriterinių optimizavimo uždavinių sprendimo būdas, leidžiantis spręsti daugiakriterinius optimizavimo uždavinius interaktyviai ir užtikrinantis gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje. Sukurta ir ištirta interaktyvi daugiakriterinių optimizavimo uždavinių sprendimų paramos sistemą, apjungianti pasiūlytą optimizavimo uždavinių sprendimo būdą, sprendimo proceso vizualizavimą ir jo lygiagretinimą. Taip pat pasiūlyta sprendimo strategija, pagal kurią sprendžiant daugiakriterinį optimizavimo uždavinį pasitelkiamas... [toliau žr. visą tekstą]

Published
2012

38. Solving Multiple Criteria Optimization Problems in an Interactive Way

Author

Filatovas, Ernestas, Kurasova, Olga, Dzemyda, Gintautas, Bareiša, Eduardas, Baronas, Romas, Čenys, Antanas, Gudas, Saulius, Mockus, Jonas, Navakauskas, Dalius, and Vilnius University

Subjects
Informatics Engineering, Daugiakriterinis optimizavimas, Sprendimų paramos sistemos, Multiple Criteria Optimization, Decision Making, Decision Support Systems, Parallel Computing, Sprendimų priėmimas, Lygiagretieji skaičiavimai
Abstract

Praktikoje dažnai tenka spręsti sudėtingus daugiakriterinius optimizavimo uždavinius, kai kriterijai būna prieštaringi, o galutinis apsisprendimas priklauso nuo sprendimų priėmėjo. Kai sprendimų priėmėjas dalyvauja sprendimo procese interaktyviai, tai jis gali koreguoti prioritetus ir siekiamus tikslus uždavinio sprendimo eigoje, kas įgalina spęsti uždavinius, turinčius daug kriterijų ir apribojimų. Be to, sprendimo priėmėjui svarbu gauti sprendinius iš visos Pareto aibės. Interaktyviam uždavinių sprendimui būtina sprendimų paramos sistema, kurios grafinė sąsaja yra pritaikyta sprendžiamam uždaviniui. Šio darbo tyrimų sritis yra interaktyvus daugiakriterinių optimizavimo uždavinių sprendimas bei sprendimų paramos sistemos. Disertacijoje nagrinėjant daugiakriterinio optimizavimo metodus, didesnis dėmesys skirtas metodams, užtikrinantiems gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje bei interaktyviems metodams. Pasiūlytas ir ištirtas daugiakriterinių optimizavimo uždavinių sprendimo būdas, leidžiantis spręsti daugiakriterinius optimizavimo uždavinius interaktyviai ir užtikrinantis gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje. Sukurta ir ištirta interaktyvi daugiakriterinių optimizavimo uždavinių sprendimų paramos sistemą, apjungianti pasiūlytą optimizavimo uždavinių sprendimo būdą, sprendimo proceso vizualizavimą ir jo lygiagretinimą. Taip pat pasiūlyta sprendimo strategija, pagal kurią sprendžiant daugiakriterinį optimizavimo uždavinį pasitelkiamas... [toliau žr. visą tekstą] In practice, optimization problems are often multiple criteria. The criteria are usually contradictory, so the final decision depends on a decision maker. When the problem is solved interactively, the decision maker can change his/her preferences in decision process. Moreover, it is important to obtain solutions from the whole Pareto front. A decision support system adapted to the specific of the problem is essential for solving multiple criteria optimization problems interactively. The objects of research are multiple criteria optimization problems, interactive methods for solving these problems, interactive decision support systems, and application of parallel computing in decision support systems. Multiple criteria optimization methods are analyzed in the dissertation. The focus of attention is the methods for a uniform distribution of solutions on the Pareto front as well as the interactive methods. An interactive way for solving multicriteria optimization problems, which finds alternative solutions uniformly distributed on the Pareto front is proposed and investigated in this dissertation. An interactive decision support system which integrates the created interactive solving way, the decision process visualization and parallelization for multiple criteria optimization is developed. The solving strategies, when a multiple criteria optimization problem is solved interactively, using a computer cluster are developed and compared experimentally. The time required for a... [to full text]

Published
2012

41. Decisions making in parallel computing

Author

Filatovas, Ernestas, Kazlauskas, Kazys, Račienė, Jurga, Lipeikienė, Joana, Dzemyda, Gintautas, Petkus, Tomas, and Vilnius Pedagogical University

Subjects
Parallel computing, Informatics, Lygiagretūs skaičiavimai, Decisions making, Sprendimų priėmimas
Abstract

The principles of parallel computing, the MPI program parcel, which was used in this work, allow to adapt them to solve tasks using computer nets and peculiarities of implanting this package. The specific character of multiple criteria tasks of optimization and way of solutions using computer nets were cleared up. The decision theory, basic methods of making decisions were described in this work.

Published
2006

45. Vaizdų klasifikavimas pagal suspaudimo algoritmo poveikį jų kokybei.

Author

Tichonov, Jevgenij, Kurasova, Olga, and Filatovas, Ernestas

Abstract

Copyright of Information Sciences / Informacijos Mokslai is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2015

49. OpenGL ir DirectX technologijų spartos palyginimas programuojant trimačius vaizdus.

Author

Filatovas, Ernestas, Melničenko, Grigorijus, and Jakštys, Vytautas

Subjects
RENDERING (Computer graphics), THREE-dimensional imaging, COMPARATIVE studies, C++, APPLICATION software
Abstract

Copyright of Information Sciences / Informacijos Mokslai is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2013
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50. 3D veido atkūrimas iš nuotraukos

Author

Kačanauskas, Mindaugas, Žiūrienė, Rytė, Filatovas, Ernestas, Baušys, Romualdas, Barauskas, Arūnas, Devenson, Jelena, Mažonavičiūtė, Ingrida, and Vilnius Gediminas Technical University

Subjects
Informatics Engineering, Geometry reconstruction, Veido nuotraukos, 3d face, 3D generic face model, Bendrinis veido modelis, Face pictures, Geometrijos atstatymas, 3d veidas
Abstract

Baigiamajame magistro darbe nagrinėjamos technologijos naudojamos 3D veido atkūrime. Pasirinkus 3D veido atkūrimą iš nuotraukos yra nagrinėjami šio proceso metu naudojami 3D bendriniai modeliai, jų sandara, bei juos naudojantys metodai. Taip pat nagrinėjamas veido savybių taškų aptikimas naudojant aktyvios formos modelius, bendrinio modelio interpoliavimas naudojant plonųjų plokštumų splainų metodą, bei ortogonalus tekstūros projektavimas modeliui. Taip pat pateikiamas naujas siūlomas veido formos modelis aprašantis papildomas savybes. Praktinėje dalyje yra pateikiama sukurta programa, kuri pateikus vieną veido nuotrauką automatiškai atkuria 3D veidą su tekstūra. Gautas 3D veidas gali būti eksportuotas į OBJ formato bylą. Gauti rezultatai yra įvertinami apskaičiuojant paklaidą tarp realių ir atkurtų veidų, naudojant lazeriu nuskaitytų 3D veidų duomenų bazę. The aim of this master thesis is to examine the technologies that are being used for 3D face reconstruction. 3D generic face models, their structure, and methods they are being used with, are being analyzed after selecting reconstruction from single photo technology. Also facial feature points detection while using active shape models, 3D generic face interpolation, and orthogonal texture mapping for model are being analyzed. Also a new facial shape model is defined. Automated 3D face reconstruction application was developed based on previous analysis. Application is capable of automatically reconstructing full 3D face model with texture from single frontal face picture. Resulting face model can be exported to another software by using OBJ file format. The results of application are being evaluated while calculating error between real and reconstructed faces, while using laser scanned 3D face database.

Published
2014

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