Acoustic Event Detection System

Hlavním cílem této práce je vytvořit systém schopný detekce, klasifikace a lokalizace střelby. Systém se skládá z dedikované desky a serverové aplikace. Systém detekuje zvukové události a jako první krok vyfiltruje události, které nejsou střelba. Následně jsou klíčové vlastnosti nahrávky extrahovány pomocí Mel-Frequency Cepstral Coefficients. Na vektoru klíčových vlastností je dále provedena klasifikace použitého kalibru zbraně, kterou provádí metoda podpůrných vektorů (Support-Vector Machine). Lokalizace střelby je prováděna na zvukových událostech, ke kterým je připojena velmi přesná časová značka (timestamp) a pozice měřícího přístroje (uzlu). Data shromážděná z jednotlivých zařízení jsou použita pro řešení lokalizačního problému na základě změřeného času zaznamenání (Time of Arrival Localization Problem). Pro jeho řešení jsou popsány dvě různé metody, lišící se dle počtu měřících zařízení, které danou událost detekovaly. Vytvořená serverová aplikace je nejen schopna řešit lokalizační úlohu popsanou výše, ale také poskytuje vizualizaci s administrací uživatelů, uzlů a zpráv uzlů. Navržená deska je schopna získat svou pozici spolu s přesnou časovou značkou události a odeslat všechny potřebné informace pomocí LoRaWan sítě na server. Na desce je naimplementován jak detekční, tak klasifikační algoritmus. Navíc deska nabízí rozhraní ve formě příkazové řádky pro nastavení parametrů aplikace, jako jsou například koeficienty detekčního algoritmu.The main goal of the thesis is to create a system capable of gunshot detection, classification, and localization. The detection system consists of a specialized board and a server application. At first, the gunshot detection algorithm is executed for filtration of non-gunshot events. Afterwards, the features are extracted by Mel-Frequency Cepstral Coefficients. The feature vector is then passed to the gunshot classification, performed through Support Vector Machine. The localization task is executed on precisely timestamped acoustic events that are coupled with position of the measuring devices (nodes) on the server. The aggregated data are utilized for solving the Time of Arrival Localization Problem. Two different methods are described based on the number of nodes that detected the event. The created server application solves the localization task as mentioned above but also offers visualization and administration of users, nodes, and node’s messages. The proposed board is able to acquire position with precise timestamping and send the required information through LoRaWAN network to the server. The board implements detection and classification algorithms and also offers a command line interface for setting the firmware’s parameters such as detection algorithms’ coefficients

An architecture for a viable information system : application of “viable system model” in modern systems architecture for the creation of viable information system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe idea of present work is born in the context of problems that nowadays organizations facing with their information systems. Modern information systems are monolithic, complex and not ready for the future challenges. But, at the same time, they are playing a key role in a chain of value delivery. Such systems are not fitting perfectly into the businesses where they are employed (not providing all desired outcomes as they are expected by the creators and users of the system). There are three main areas where critical problems can arise in the process of information systems development: modelling, managing, and maintaining information systems. Once an architecture of the system is designed and implemented, the organization faces several problems in maintaining it. It is important to notice that, most of the time, people do not approach ISs problems in a systemic thinking way, but instead in a reductionist way. Which means that no one trying to understand all the interactions between components of the system, which may lead to very interesting and useful findings when studied properly. But the information system it’s just a system, sometimes very complex and monolithic, but it’s still just a system. In this work, Viable System Model will be used to help solve problems described above. The goal of this work is to apply VSM to the Information artifacts to take advantage of all benefits VSM offers. The main outcome from this work will be an architecture for Viable Information System. Systems thinking area will be taken as a basis for the development of the ideas presented in this dissertation. We are proposing a new architecture for modern organizations to use, to take advantage of benefits that VSM must offer. Namely: understanding of complexity, resilience to change, survival to an external environment and ability to exist independently of its external environment. Given that Information System is a system, and, so, obeys laws of general system theory, we could take advantage of using the “Viable System Model” architecture

A general framework for implicit and explicit debiasing of distributional word vector spaces

Distributional word vectors have recently been shown to encode many of the human biases, most notably gender and racial biases, and models for attenuating such biases have consequently been proposed. However, existing models and studies (1) operate on under-specified and mutually differing bias definitions, (2) are tailored for a particular bias (e.g., gender bias) and (3) have been evaluated inconsistently and non-rigorously. In this work, we introduce a general framework for debiasing word embeddings. We operationalize the definition of a bias by discerning two types of bias specification: explicit and implicit. We then propose three debiasing models that operate on explicit or implicit bias specifications and that can be composed towards more robust debiasing. Finally, we devise a full-fledged evaluation framework in which we couple existing bias metrics with newly proposed ones. Experimental findings across three embedding methods suggest that the proposed debiasing models are robust and widely applicable: they often completely remove the bias both implicitly and explicitly without degradation of semantic information encoded in any of the input distributional spaces. Moreover, we successfully transfer debiasing models, by means of cross-lingual embedding spaces, and remove or attenuate biases in distributional word vector spaces of languages that lack readily available bias specifications