Universal proxy for storing data in a cloud

Dotā bakalaura darbā ir aprakstīts universāls starpnieks datu glabāšanai mākonī, kas palīdz risināt “piesaistes pie pakalpojumu sniedzēja”(“vendor lock-in”) problēmu. Starpnieks būtībā ir abstrakcijas slānis datu glabāšanas mākoņu pakalpojumiem, kas ļauj izstrādāt lietotnes, kuri nav atkarīgi no konkrētiem pakalpojumu sniedzējiem. Autors izpētīja dažādus datu glabāšanas pakalpojumus, to API un definēja universālu starpnieku datu glabāšanai mākonī, starpnieka protokolu, starpnieka augša līmeņa arhitektūru un uzdevumus. Lai pārbaudīt, ka starpnieks patiešam var risināt uzdoto problēmu, darba ietvaros tika izstrādātas starpnieka prototips un divas lietotnes, kas izmanto starpnieku lai piekļūtu lietotāja failiem mākonī: vienkārša tīmekļa lietotne un Windows virtuālā failu sistēma.The aim of this thesis is to describe universal proxy for storing data in a cloud that can be used to solve “vendor lock-in” problem. Basically, proxy is an abstraction layer for storing and accesing data that allows to create applications which can use different data storage services and don't depend on specific service providers. Author investigated various data storage services, their API and defined universal proxy for storing data in a cloud: it's protocol, top-level architecture and goals. In order to determine if proxy is really able to solve posed problem author developed proxy prototype and two applications, web application and Windows virtual file system, that use proxy to access user's files in a cloud. Author analyzed achieved results, identified advantages and disadvantages of proxy solution, compared with similar works

Latviešu valodas modelēšana automātiskai runas atpazīšanai

Pēdējo gadu laikā runas atpazīšanas tehnoloģiju panākumi tādām valodām kā angļu valoda ir izraisījuši satraukumu un jaunu interesi. Šie panākumi ir mudinājuši daudzus izstrādātājus pievērsties runas tehnoloģijām savai dzimtajai valodai. Tomēr lielākā daļa pētījumu ir koncentrēti ap “lielajām” valodām, bet tādas valodas kā latviešu nav aptvertas. Šīs doktora disertācijas mērķis ir atrast efektīvas un optimālas metodes vispiemērotāko modeļu un sistēmu radīšanai latviešu valodas vispārīgai runas atpazīšanai. Darbā analizēti gan teorētiskie, gan praktiskie aspekti: akustisko un valodu modeļu izpēte, sistēmu pielāgošana īpašiem uzdevumiem, automātiska datu vākšana, apgrieztā teksta normalizācija (interpunkcijas atjaunošana) un praktisku sistēmu izstrāde. Uz vispārīgas jomas novērtēšanas kopas darbā izstrādāta sistēma sasniedz kļūdas īpatsvaru 10,1%, un ievērojami pārsniedz Google (36,2 –50,6%) un Speechmatics (25,2%) risinājumus latviešu valodai.In recent years, the success of speech technologies like speech recognition and speech synthesis for languages like English has prompted a new excitement about spoken interfaces and an interest in further research of these technologies. However, most of the research and development are concentrated around “big” languages and languages like Latvian are not covered. The aim of this doctoral thesis is to research methods and models for automatic speech recognition for Latvian language. Both theoretical and practical aspects are covered, including a research on acoustic and language models, system adaptation for specific tasks, automatic data collection and augmentation, inverse text normalization (punctuation restoration) and practical system development. On a general domain evaluation set the developed system achieves a word error rate of 10.1% and significantly outperforms Google (error rate of 36.2-50.6%) and Speechmatics (error rate of 25.2%) solutions for Latvia

Using Inverse Reinforcement Learning Methods in Intelligent Agent Development

Dota maģistra darba mērķis ir izpētīt inversās pastiprinājuma vadītas apmācīšanas metodi no intelektuālo aģentu izstrādes perspektīvas. Darba ietvaros tika izpētīti parastas pastiprinājuma vadītas apmācības teorētiskie pamati, inversās pastiprinājuma vadītas apmācības formulējums, motivācija, iespējamie risinājumi un konkrēti algoritmi. Iegūtās zināšanas tiek izmantotas darba praktiskajā daļā, kur autors izstrādājis un apmācījis intelektuālo aģentu, kas prot spēlēt Mario datorspēli. Konkrētāk, autors vairākas reizes nodemonstrēja aģentam, kā spēlēt Mario, un balstoties uz šīm demonstrācijām, aģents iemācījās to izpildīt. Mario aģenta izstrādes gaitā tika identificētas dažādas problēmas un grūtības, kuras parasti rodas, praktiski pielietojot inversās pastiprinājuma vadītas apmācīšanas algoritmus. Darba otra daļa ir veltīta autora piedāvātiem klasisko inversās pastiprinājuma vadītas apmācīšanas algoritmu uzlabojumiem, kas palīdz atrisināt vienu no šīm problēmām.The aim of this work is to study inverse reinforcement learning and apply it to develop and train intelligent agent for Mario game. In the course of this work author studies reinforcement learning theoretic basics, inverse reinforcement learning problem formulation, motivation, possible solutions and few concrete algorithms. Obtained knowledge is used in practical part of this work, where author developed and trained intelligent agent which can play Mario game. Concretely, author recorded several demonstrations of how to play Mario game, and then this demonstrations where used by agent to “understand” the task and learn how to do it. In the course of Mario agent development several difficulties and problems were identified, which usually arise when using inverse reinforcement learning algorithms in practice. In the second part of this work author offers improvements to classic inverse reinforcement learning algorithm which helps to solve one of these problems

Using Inverse Reinforcement Learning Methods in Intelligent Agent Development

Dota maģistra darba mērķis ir izpētīt inversās pastiprinājuma vadītas apmācīšanas metodi no intelektuālo aģentu izstrādes perspektīvas. Darba ietvaros tika izpētīti parastas pastiprinājuma vadītas apmācības teorētiskie pamati, inversās pastiprinājuma vadītas apmācības formulējums, motivācija, iespējamie risinājumi un konkrēti algoritmi. Iegūtās zināšanas tiek izmantotas darba praktiskajā daļā, kur autors izstrādājis un apmācījis intelektuālo aģentu, kas prot spēlēt Mario datorspēli. Konkrētāk, autors vairākas reizes nodemonstrēja aģentam, kā spēlēt Mario, un balstoties uz šīm demonstrācijām, aģents iemācījās to izpildīt. Mario aģenta izstrādes gaitā tika identificētas dažādas problēmas un grūtības, kuras parasti rodas, praktiski pielietojot inversās pastiprinājuma vadītas apmācīšanas algoritmus. Darba otra daļa ir veltīta autora piedāvātiem klasisko inversās pastiprinājuma vadītas apmācīšanas algoritmu uzlabojumiem, kas palīdz atrisināt vienu no šīm problēmām.The aim of this work is to study inverse reinforcement learning and apply it to develop and train intelligent agent for Mario game. In the course of this work author studies reinforcement learning theoretic basics, inverse reinforcement learning problem formulation, motivation, possible solutions and few concrete algorithms. Obtained knowledge is used in practical part of this work, where author developed and trained intelligent agent which can play Mario game. Concretely, author recorded several demonstrations of how to play Mario game, and then this demonstrations where used by agent to “understand” the task and learn how to do it. In the course of Mario agent development several difficulties and problems were identified, which usually arise when using inverse reinforcement learning algorithms in practice. In the second part of this work author offers improvements to classic inverse reinforcement learning algorithm which helps to solve one of these problems

Comparison of deep learning approaches for Lithuanian sentiment analysis

Sentiment analysis is one of the oldest Natural Language Processing problems, still relevant and challenging today. It is usually formulated and solved as a supervised machine learning problem. In this research, we are solving the three-class sentiment analysis problem for the non-normative Lithuanian language. The contribution of our research is related to applying the innovative BERT-based multilingual sentence transformer models to the Lithuanian sentiment analysis problem. For comparison purposes, we have also investigated traditional Deep Learning approaches, such as fastText or BERT word embeddings with the Convolutional Neural Network as the classifier. The best accuracy ∼0.788 was achieved with the purely monolingual model, i.e., fastText (trained on the very large and diverse Lithuanian corpus) and the Convolutional Neural Network (refined in various text classification tasks). The backbone of the second-best approach (reaching ∼0.762) is the multilingual sentence-transformer-based model, which is the trend in text classification tasks, especially for the English language. Keywords: Sentiment analysis, monolingual vs. multilingual models, word vs. sentence embeddings, transformer models, the Lithuanian language

General-purpose Lithuanian automatic speech recognition system

This paper describes the development of a general-purpose automatic speech recognition system for Lithuanian. The system is capable of performing both the transcription of user submitted audio recordings and real-time speech-totext conversion. The comparative evaluation results prove that the presented system outperforms all other ASR systems for the Lithuanian language. The system also includes number and date normalization and is paired with an automatic punctuation restoration model that achieves state-of-the-art results for the Lithuanian language. Importantly, the system is publicly available to any Lithuanian speaker for testing via its web-page and mobile application

Monolingual and cross-lingual intent detection without training data in target languages

International audienceDue to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by themachine translating it from another language; (2) there are cross-lingual solutions that work without the training data in the target language. Consequently, in this research, we use the English dataset and solve the intent detection problem for five target languages (German, French, Lithuanian, Latvian, and Portuguese). When seeking the most accurate solutions, we investigate BERT-based word and sentence transformers together with eager learning classifiers (CNN, BERT fine-tuning, FFNN) and lazy learning approach (Cosine similarity as the memory-based method). We offer and evaluate several strategies to overcome the data scarcity problem with machine translation, crosslingual models, and a combination of the previous two. The experimental investigation revealed the robustness of sentence transformers under various cross-lingual conditions. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered ourtop-line. However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages

General-purpose Lithuanian automatic speech recognition system

Knygos ISBN 978-1-61499-912-6 (online)This paper describes the development of a general-purpose automatic speech recognition system for Lithuanian. The system is capable of performing both the transcription of user submitted audio recordings and real-time speech-totext conversion. The comparative evaluation results prove that the presented system outperforms all other ASR systems for the Lithuanian language. The system also includes number and date normalization and is paired with an automatic punctuation restoration model that achieves state-of-the-art results for the Lithuanian language. Importantly, the system is publicly available to any Lithuanian speaker for testing via its web-page and mobile applicationTaikomosios informatikos katedraVytauto Didžiojo universiteta