隨著電腦音樂技術的蓬勃發展，合乎情境需求的音樂若被能自動推薦給使用者，將是知識工作者所樂見的。我們提出了一個定義使用者操作情境的情境塑模，定義使用者操作情境，並利用累計專注視窗的轉變，找出使用者的操作情境。同時，我們也提出了音樂推薦塑模，依據使用者的操作情境與聆聽的音樂，分析探勘情境與音樂特徵間的關聯特性，利用探勘出的關聯推薦適合情境的音樂給使用者。在此音樂推薦塑模中，我們採用Content-based Recommendation的作法。我們分析音樂的特徵值，並發展MAML(Multi-attribute Multi-label)的分類演算法以及Probability Measure二種方法來探勘情境屬性與音樂特徵間的關聯特性。根據探勘出的關聯特性，找出適合情境的音樂特徵，再從音樂資料庫中推薦符合音樂特徵的音樂給使用者。本論文的符合使用者操作情境的音樂推薦系統是利用Windows Hook API實作。經實驗證明，本論文方法在符合情境的音樂推薦上，擁有近七成準確率。 / With the development of digital music technology, knowledge workers will be delighted if the music recommendation system is able to automatically recommend music based on the operating context in the desktop. The context model and context identification algorithm are proposed to define the operating context of users and to detect the transition of context based on the changes of focused windows. Two association discovery mechanisms, MMAL (Multi-attribute Multi-label) algorithm and PM (Probability Measure), are proposed to discover the relationships between context features and music features. Based on the discovered rules, the proposed music recommendation mechanism recommends music to the user from the music database according to the operating context of users. The context-based recommendation system is implemented using Windows Hook API. Experimental results show that near 70% accuracy can be achieved.

Approved for public release, distribution is unlimited / This thesis study focuses on understanding the dissipation processes of the stratocumulus deck after sunrise. This objective is met through careful analyses of observational data as well as model simulations. Measurements from the Marine Atmosphere Measurement Lab (MAML) of the Naval Postgraduate School (NPS) are used in this study. In particular, the half-hourly wind profiler/Radio Acoustic Sounding System (RASS) measurements were used to determine the boundary layer top and the evolution of the boundary layer mean thermodynamic properties during the cloud breakup period. Measurements from a laser ceilometer and the routine surface measurements are also used to detect the variation of cloud base height, the evolution of the cloud deck, and the onset of sea breeze. These measurements revealed the increase of the boundary layer depth after sunrise followed by a decrease of the boundary layer depth after the onset of the sea breeze, which points to the role of surface heating and sea breeze development in modulating cloud evolution. The effects of surface heating and sea breeze are further tested using a 1-dimensional mixed layer model modified for coastal land surfaces. / Lieutenant Commander, United States Navy

This thesis describes the modeling of Martial Arts as a markup language. Up until now Martial Arts has already been documented in books, videos, tradition and other methods. Though to represent Martial Arts knowledge consistently and uniformly in a digital era, we introduce the Martial Arts Markup Language (MAML), which is based on XML. Because XML provides a standardized, serializable and portable format, MAML also enables sharing among students, teachers and their peers across different platforms, media and networks. MAML provides the ability, with appropriate XML tools, to document a Martial Arts style in a structured way. To achieve this, we first analyze the aspects that comprise Martial Arts; and how its states and processes relate to one another. We model in MAML describing the stances, transitions, punches, blocks, techniques, combinations, reactions and patterns used in Martial Arts. We discuss the implementation of MAML by observing and extracting the definable aspects in existing Martial Art Instructive Documents. The MAML Schema assures that the details of a Martial Arts Style’s elements are consistent. Current simulation efforts will be explained as well as areas for future development. We have described Martial Arts by observing what has already been done and creating a structured standard to document them. We hope to enable practitioners’ abilities to learn from and develop their arts by providing a resource in which they can interact with. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Managing a system of network services is a complex and large-scale task that often lacks a trivial optimal solution. Deep Reinforcement Learning (RL) has shown great potential in being able to solve these tasks in static settings. However, in practice, the RL agents struggle to generalize their control policies enough to work in more dynamic real-world environments. To achieve a generality between environments, multiple contributions are made by this thesis. Low-level metrics are collected from each node in the system to help explain changes in the end-to-end delay of the system. To achieve generality in its control policy, more ways to observe and understand the dynamic environment and how it changes are provided to the RL agent by introducing the end-to-end delay of each service in the system to its observation space. Another approach to achieving more generality in RL policies is Model-Agnostic Meta-Learning (MAML), a type of Meta-Learning approach where instead of learning to solve a specific task, the model learns to learn how to quickly solve a new task based on prior knowledge. Results show that low-level metrics yield a much greater generality when helping to explain the delay of a system. Applying MAML to the problem is beneficial in adding generality to a learned RL policy and making the adaptation to a new task faster. If the RL agent can observe the changes to the underlying dynamics of the environment between tasks by itself, the policy can achieve this generality by itself without the need for a more complex method. However, if acquiring or observing the necessary data is too expensive or complex, switching to a Meta-Learning approach might be beneficial to increase generality. / Hanteringen av ett system med nätverksstjänster är ett komplext och stor skaligt problem där den optimal lösning inte är trivial. Djup förstärkningsinlärning har visat stor potential i att kunna lösa dessa problem i statiska miljöer. Dock är det svårt att generalisera lösningarna tillräckligt för att fungera i mer komplicerade och realistiska dynamiska miljöer. För att uppnå mer generella lösningar mellan miljöer presenterar denna masteruppsats flera möjliga lösningar. Lågnivåmetrik samlas in från varje nod i systemet för att hjälpa förklara skillnader i den totala responstiden för varje tjänst i systemet. För att generalisera förstärkningsinlärningsmodellen kan den förses med fler sätt att observera miljön, och därmed lära sig förstå hur den dynamiska miljön förändras. En annan metod för att uppnå mer generalitet inom förstärkningsinlärning är Model-Agnostic Meta-Learning (MAML), en typ av Meta-Learning där istället för att lära sig att lösa en specifik uppgift, modellen lär sig att lära sig att snabbt lösa en ny uppgift baserat på sin tidigare kunskap. Resultaten visar att lågnivåmetriken leder till en mycket högre generalitet i att hjälpa till att förklara responstiden av ett system. Att applicera MAML till problemet hjälper att bidra med generalitet till en förstärkningsinlärningsmodell och gör anpassningen till en ny uppgift snabbare. Om modellen själv kan observera ändringarna i underliggande dynamiken bakom miljön mellan uppgifter kan den uppnå mer generalitet utan ett behov av en mer komplex metod som MAML. Däremot, om det är svårt eller dyrt att få tag på eller observera den nödvändiga datan, kan ett byte till en Meta-Learning baserad metod vara mer fördelaktig för att öka generaliteten.