Design of a highly scalable architecture for location-aware API development in the cloud

We use services which use our location information to improve user experience on daily basis. Location-based services are well defined and can be built based on several existing architectures, especially in the form of a monolithic solution. Poor scalability and inability to handle immense loads are problems which existing architectures face today. Existing architectures also don't provide an option to define and develop location based APIs. In this master thesis we defined an architecture which allows the definition and development of APIs that respond based on user location. Key feature of the API is access control and adjusting functions based on user location. We based the architecture on microservices with the aim of achieving elastic scalability, high availability, understandability and decoupling. By using microservices and other architectural design patterns we designed an architecture which uses cloud concepts and enables high horizontal scalability. We validated the success of the architecture design by verifying the possibility of implementing an REST API with different behavior depending on the user location, the possibility of automatic scaling of microservices in the cloud and by verifying the design patterns

28. evropska poletna šola jezika, logike in informatike ESSLLI 2016

28. evropska poletna šola jezika, logike in informatike ESSLLI 201

Aplikacije za učenje pravilne izgovorjave

Aplikacije za učenje pravilne izgovorjav

Design of a highly scalable architecture for location-aware API development in the cloud

Danes že na dnevni ravni uporabljamo storitve s kontekstno informacijo o lokaciji za izboljšanje uporabniške izkušnje. Lokacijske storitve so dobro opredeljene in zanje obstaja več arhitektur, predvsem v obliki monolitnih rešitev. Težava obstoječih arhitektur je v njihovi slabi skalabilnosti in nezmožnosti prilagajanju ogromni količini zahtevkov, ki smo ji priča danes. Težava obstoječih arhitektur se kaže tudi v nezmožnosti definicije in razvoja vmesnikov API, ki se odzivajo glede na lokacijo. V magistrski nalogi smo definirali arhitekturo, ki omogoča definicijo in razvoj lokacijsko odvisnih vmesnikov API tipa REST. Ključna lastnost vmesnikov API je določanje pravic dostopa in prilagajanje delovanja glede na lokacijo uporabnika. Pri definiciji arhitekture smo sledili konceptom mikrostoritev s ciljem doseganja elastične skalabilnosti, visoke razpoložljivosti, razumljivosti in neodvisnosti. Z uporabo koncepta mikrostoritev ter ostalih arhitekturnih in načrtovalskih vzorcev smo zasnovali arhitekturo, ki temelji na konceptih oblaka in omogoča visoko horizontalno skalabilnost. Uspešnost arhitekture smo potrdili z verificiranjem zmožnosti definicije vmesnika API tipa REST z različnim obnašanjem glede na lokacijo uporabnika, z možnostjo samodejnega skaliranja mikrostoritev v oblaku in z verifikacijo načrtovalskih vzorcev.We use services which use our location information to improve user experience on daily basis. Location-based services are well defined and can be built based on several existing architectures, especially in the form of a monolithic solution. Poor scalability and inability to handle immense loads are problems which existing architectures face today. Existing architectures also don\u27t provide an option to define and develop location based APIs. In this master thesis we defined an architecture which allows the definition and development of APIs that respond based on user location. Key feature of the API is access control and adjusting functions based on user location. We based the architecture on microservices with the aim of achieving elastic scalability, high availability, understandability and decoupling. By using microservices and other architectural design patterns we designed an architecture which uses cloud concepts and enables high horizontal scalability. We validated the success of the architecture design by verifying the possibility of implementing an REST API with different behavior depending on the user location, the possibility of automatic scaling of microservices in the cloud and by verifying the design patterns

Design of a highly scalable architecture for location-aware API development in the cloud

We use services which use our location information to improve user experience on daily basis. Location-based services are well defined and can be built based on several existing architectures, especially in the form of a monolithic solution. Poor scalability and inability to handle immense loads are problems which existing architectures face today. Existing architectures also don't provide an option to define and develop location based APIs. In this master thesis we defined an architecture which allows the definition and development of APIs that respond based on user location. Key feature of the API is access control and adjusting functions based on user location. We based the architecture on microservices with the aim of achieving elastic scalability, high availability, understandability and decoupling. By using microservices and other architectural design patterns we designed an architecture which uses cloud concepts and enables high horizontal scalability. We validated the success of the architecture design by verifying the possibility of implementing an REST API with different behavior depending on the user location, the possibility of automatic scaling of microservices in the cloud and by verifying the design patterns

Computer vision based automatic fruit recognition

The topic of the thesis was analysing the suitability of different algorithms from computer vision for fruit recognition. Fruit provides a challenging domain for recognition due to its diversity of fruits of the same class, the similarity of fruits of different classes and the sheer number of different fruits. For an efficient fruit recognition it was necessary to describe the images with good descriptions. Color, texture, size and shape information of fruits were obtained through a variety of descriptors. The images were then classified based on the obtained descriptors with well known classification methods from machine learning. For a great performance of classification methods a good fruit image database was needed. Since such a publicly available image database does not exist, an image database had to be obtained. Based on the analysis in the thesis a fruit recognition system was built with an 85% success rate on the obtained image database

Alternative Endings of Slovene Verbs in Third Person Plural: A Corpus Approach

Abstract This paper is concerned with the alternative endings of some Slovene verbs in third person plural. Certain Slovene verbs in this form can take the endings -jo or -do (jejo or jedo), whereby the two possible choices are normatively evaluated in various ways in Slovene language manuals. The paper introduces a corpus-driven approach to the problem by first extracting potential verbs with this phenomenon, after which the use of both endings is compared in the subcorpora of Janes (a corpus of Slovene user-generated content) and in the Kres corpus

Training corpus ssj500k 2.2

The ssj500k training corpus contains about 500,000 tokens manually annotated on the levels of tokenisation, sentence segmentation, morphosyntactic tagging, and lemmatisation. About half of the corpus is also manually annotated with syntactic dependencies, named entities, and verbal multiword expressions. About a quarter of the corpus is annotated with semantic role labels. The morphosyntactic tags and syntactic dependencies are included both in the JOS/MULTEXT-East framework, as well as in the framework of Universal Dependencies. The annotations of the ssj500k corpus follow (1) the MULTEXT-East V6 morphosyntactic specifications for Slovene, http://nl.ijs.si/ME/V6/msd/, (2) the JOS dependency schema, http://nl.ijs.si/jos/bib/jos-skladnja-navodila.pdf, the Universal Dependencies morphosyntactic specifications and syntactic dependencies for Slovene-SSJ, https://universaldependencies.org/, (4) the Janes annotation guidelines for Slovenian named entities, http://nl.ijs.si/janes/wp-content/uploads/2017/09/SlovenianNER-eng-v1.1.pdf, and (5) the Guidelines of the PARSEME shared task on verbal multiword expressions, http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.1/ The vocabulary of (1) and (2) is provided in the back element and (3), (4), and (5) in the teiHeader of the TEI encoded corpus. The semantic role labels are also documented in the teiHeader. In contrast to the previous version 2.1, this version corrects various errata in spacing and text metadata and adds UD morphological and (where it was possible to do so automatically) dependency annotations to the corpus. Note that the UD annotations are not included in the vertical file