Vykopirovka s plana uchastka kolonizat︠s︡ionnogo fonda N 417-18-22, Khersonskogo okruga Skadovskogo raĭona pod nazvaniem "3-e Kustovoe Obʻedenenie" predtoznachennogo Narkomzemzemom pod perselenie evreev

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Vykopirovka s plana uchastka kolonizat︠s︡ionnogo fonda N 975b Khersonskogo okruga Skadovskogo raĭona, pod nazvaniem "1 Kustovoe Obʻedenenie" prenaznachennogo Narkomzemom dli︠a︡ perselenii︠a︡ evreev

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Plan zemelʻnogo uchastka 1-go Kakhovskogo meliorativnogo tovarishchestva

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Plan Ekadovskogo Kooperatvnogo Meliorativnogo tovarishchestva PUCH

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Using Mathematical Morphology for Geometric Music Retrieval

The usual task in music information retrieval (MIR) is to find occurrences of a monophonic query pattern within a music database, which can contain both monophonic and polyphonic content. The so-called query-by-humming systems are a famous instance of content-based MIR. In such a system, the user's hummed query is converted into symbolic form to perform search operations in a similarly encoded database. The symbolic representation (e.g., textual, MIDI or vector data) is typically a quantized and simplified version of the sampled audio data, yielding to faster search algorithms and space requirements that can be met in real-life situations. In this thesis, we investigate geometric approaches to MIR. We first study some musicological properties often needed in MIR algorithms, and then give a literature review on traditional (e.g., string-matching-based) MIR algorithms and novel techniques based on geometry. We also introduce some concepts from digital image processing, namely the mathematical morphology, which we will use to develop and implement four algorithms for geometric music retrieval. The symbolic representation in the case of our algorithms is a binary 2-D image. We use various morphological pre- and post-processing operations on the query and the database images to perform template matching / pattern recognition for the images. The algorithms are basically extensions to classic image correlation and hit-or-miss transformation techniques used widely in template matching applications. They aim to be a future extension to the retrieval engine of C-BRAHMS, which is a research project of the Department of Computer Science at University of Helsinki.

Skhematicheskiĭ chertezh Meliorativnogo tovarishchestva "VIN-SAD"

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Plan zemelʻnogo uchastka Cholvaskogo kooperativnogo meliorativnogo tovarishchestva "Intensivn. N 2 Khersonskogo okruga Skadovskogo raĭona urochishch. "Kara-Bulat"

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Plan Aleshkovskogo meliorativnogo tovarishchestva

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Sketch map of settlements in the Skadovsʹkyĭ region

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Plan Golo-pristanskogo meliorativnogo tovarishchestva

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Karta Khersonskogo okruga Odesskoǐ gubernii

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AndroMedia - Towards a Context-aware Mobile Music Recommender

Portable music players have made it possible to listen to a personal collection of music in almost every situation, and they are often used during some activity to provide a stimulating audio environment. Studies have demonstrated the effects of music on the human body and mind, indicating that selecting music according to situation can, besides making the situation more enjoyable, also make humans perform better. For example, music can boost performance during physical exercises, alleviate stress and positively affect learning. We believe that people intuitively select different types of music for different situations. Based on this hypothesis, we propose a portable music player, AndroMedia, designed to provide personalised music recommendations using the user’s current context and listening habits together with other user’s situational listening patterns. We have developed a prototype that consists of a central server and a PDA client. The client uses Bluetooth sensors to acquire context information and logs user interaction to infer implicit user feedback. The user interface also allows the user to give explicit feedback. Large user interface elements facilitate touch-based usage in busy environments. The prototype provides the necessary framework for using the collected information together with other user’s listening history in a context- enhanced collaborative filtering algorithm to generate context-sensitive recommendations. The current implementation is limited to using traditional collaborative filtering algorithms. We outline the techniques required to create context-aware recommendations and present a survey on mobile context-aware music recommenders found in literature. As opposed to the explored systems, AndroMedia utilises other users’ listening habits when suggesting tunes, and does not require any laborious set up processes.

Karta chasti Khersonskogo okruga Odesskoĭ gubernii

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Map of the Staro-Shvet︠s︡ka colony

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Map of the Shvedsʻka- Poli︠a︡na colony

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