RESUMEN Se realizó un estudio descriptivo observacional retrospectivo en población hospitalaria con el objetivo de identificar los factores asociados a la recidiva del pie Bot tratado con el método de Ponseti en el Hogar Clínica San Juan de Dios entre los años 2009-2012. Se encontraron datos en las historias clínicas de 128 pacientes con pie Bot que cumplieron los criterios de selección del estudio. Se presentaron los datos en tablas y gráficos con distribución porcentual y se realizó un análisis estadístico descriptivo con prueba de T de Student para las variables numéricas y Chi cuadrado para las variables cualitativas con un nivel de significancia estadística p<0.05. La edad media de los pacientes fue de 4.9+-6.7 años. La recidiva fue del 10.2%. Los factores asociados a recidiva fueron: mayor edad media (6.4+-5.6 meses), ser del sexo masculino(76.9%), proceder de Provincias(53.8%), madres con colaboración regular (23.1%), pie equino típico (76.9%), pie izquierdo afectado (46.2%), severidad grave (84.6%), valoración radiográfica grave (23.1%), abandono precoz (92.3%), no uso de férula (100%), no tenotomía amplia (30.8%) y deficiente terapia física (53.8%). Conclusiones: La frecuencia de recidiva del pie Bot fue similar a lo reportado a nivel mundial y los factores asociados a recidiva hallados por el estudio fueron: edad de 6.4+-5.6 meses al iniciar la corrección, poca colaboración de la madre, procedente de provincias, pie Bot atípico, pie Bot izquierdo, severidad grave, no uso de férula, abandono precoz del tratamiento y deficiente terapia física.

Proefschrift Maastricht. / Met lit. opg. - Met een samenvatting in het Nederlands.

Machine learning has shown great success in building security applications including bot detection. However, many machine learning models are difficult to deploy since model training requires the continuous supply of representative labeled data, which are expensive and time-consuming to obtain in practice. In this thesis, we build a bot detection system with a data synthesis method to explore detecting bots with limited data to address this problem. We collected the network traffic from 3 online services in three different months within a year (23 million network requests). We develop a novel stream-based feature encoding scheme to support our model to perform real-time bot detection on anonymized network data. We propose a data synthesis method to synthesize unseen (or future) bot behavior distributions to enable our system to detect bots with extremely limited labeled data. The synthesis method is distribution-aware, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data. We show that data synthesis also improves the model's sustainability over time and speeds up the retraining. Finally, we compare data synthesis and adversarial retraining and show they can work complementary with each other to improve the model generalizability. / Master of Science / An internet bot is a computer-controlled software performing simple and automated tasks over the internet. Although some bots are legitimate, many bots are operated to perform malicious behaviors causing severe security and privacy issues. To address this problem, machine learning (ML) models that have shown great success in building security applications are widely used in detecting bots since they can identify hidden patterns learning from data. However, many ML-based approaches are difficult to deploy since model training requires labeled data, which are expensive and time-consuming to obtain in practice, especially for security tasks. Meanwhile, the dynamic-changing nature of malicious bots means bot detection models need the continuous supply of representative labeled data to keep the models up-to-date, which makes bot detection more challenging. In this thesis, we build an ML-based bot detection system to detect advanced malicious bots in real-time by processing network traffic data. We explore using a data synthesis method to detect bots with limited training data to address the limited and unrepresentative labeled data problem. Our proposed data synthesis method synthesizes unseen (or future) bot behavior distributions to enable our system to detect bots with extremely limited labeled data. We evaluate our approach using real-world datasets we collected and show that our model outperforms existing methods using only 1% of the labeled data. We show that data synthesis also improves the model's sustainability over time and helps to keep it up-to-date easier. Finally, we show that our method can work complementary with adversarial retraining to improve the model generalizability.

As online gaming becomes more popular, it has also become increasingly important to identify and remove those who leverage automated player systems (bots). Manual bot detection depends on the ability of game administrators to differentiate between bots and normal players. The objective of this thesis was to determine whether expert poker players can differentiate between bot and human players in Texas Hold ‘Em Poker. Participants were deceived into thinking a number of bots and humans were playing in gameplay videos and asked to rate player botness and skill. Results showed that participants made similar observations about player behaviour, yet used these observations to reach differing conclusions about whether a given player was a bot or a human. These results cast doubt on the reliability of manual bot detection systems for online poker, yet also show that experts agree on what constitutes skilled play within such an environment.

abstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible, because the bots may get suspended or deleted, bots should be observed in smaller groups, based on their characteristics, as types. Yet, most of the existing research on social media bot detection is focused on labeling bot accounts by only distinguishing them from human accounts and may ignore differences between individual bot accounts. The consideration of these bots' types may be the best solution for researchers and social media companies alike as it is in both of their best interests to study these types separately. However, up until this point, bot categorization has only been theorized or done manually. Thus, the goal of this research is to automate this process of grouping bots by their respective types. To accomplish this goal, the author experimentally demonstrates that it is possible to use unsupervised machine learning to categorize bots into types based on the proposed typology by creating an aggregated dataset, subsequent to determining that the accounts within are bots, and utilizing an existing typology for bots. Having the ability to differentiate between types of bots automatically will allow social media experts to analyze bot activity, from a new perspective, on a more granular level. This way, researchers can identify patterns related to a given bot type's behaviors over time and determine if certain detection methods are more viable for that type. / Dissertation/Thesis / Presentation Materials for Thesis Defense / Masters Thesis Computer Science 2019

Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.

由於各國政府有關經常部門支出之大量增加，往往造成政府之財政負擔加大；同時，公部門之欠缺行政效率常為各國施政之通病，因此，以民營化的方式來進行公共建設的方法，越來越受到各國政府的支持；B.O.T.制度就是其中一種方式。 本文的目的就在於探究B.O.T.投資案的選擇權以及權利金高低的決定因素。藉由複式選擇權評價模式的協助之下，以及臺灣高速鐵路B.O.T.投資案的實際資料佐證下，一方面試圖解析B.O.T.專案所隱涵的權利金有多大，另一方面也提供企業財團評估B.O.T.投資專案獲利性之工具，同時，也能為政府在設計B.O.T.投資專案時，如何能提供給企業更大的誘因、以及如何提昇政府資源的有效利用上，一些實用性的建議。 根據本研究個案的實證結果，可以歸納出以下幾點的結論：（一）若廠商參與競標BOT投資案未能獲得建造、經營權利，投資廠商的最大損失僅限於第一個選擇權的權利金；（二）時間的長短會影響投資廠商投資的意願及願意支付的權利金金額；（三）營運期間收入越高，抑或營運期間經營成本越低，廠商便願意支付較高的權利金；（四）BOT投資案的財務風險高，不確定性的降低是廠商與有關當局的要務。

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Abstract The sewage sewerage system is an important public infrastructure actively pushed by the government presently, since it can improve our environmental quality and promote the nation¡¦s competitiveness. To minimize the government's short-term financial burden and attract the private capital, vigor, and efficient management, the Executive Yuan encourages the private sector to participate the sewage sewer construction plans via the BOT (Build-Operate-Transfer) model for those systems above 10,000 tons capability per day. It is estimated there are totally over 36 such systems in the country. This research consists of two parts. The first part deals with the feasibility study of the BOT model for the sewage sewer in Taitung city. Three factors, including the law, the risk, and the financing were assessed. In the aspect of the law, the completeness of related regulations, the timetable of local government¡¦s plan, the sewage sewer¡¦s fee, and others were important items for the success of the task. In the aspect of the risk, the allocation of risks in the various stages is the important item. Finally, the financial feasibility studies were conducted for the Nantz and Danshuei demonstrated cases, in which the government expenditure and the operation net income of the private part are the important items. Presently, the relevant BOT case documents and materials show that the financial structure is one of the key assessment elements provided that the promotion of the trade and investment, and the sign of contract are successful. So the second part of this research involved the questionnaire survey as to analyze the key factors that are beneficial for the success of the promotions of trade and investment, and the sign of contract. Results show that the successful factors include the favorable financial guarantees, the lowest guaranteed operation amount of sludge, and the take-hold of the construction land by the government. All these factors should be carefully assessed and planned in advance, together with the well-designed coordination mechanism for the agreement/contract. Keywords: Sewage sewer system, privatization, BOT model

The finance of our government that the remaining situation beginning changes into the budget deficit since 1990 . So introduced solved financial predicament experience from foreign. Britain promoted ¡§Private Finance Initiative (PFI)¡¨since 1992, it is deep and wide to influence to the development of Britain's politics, economy and society. Our country makes ¡§The Reward Non-governmental to Participate in Traffic Build Regulations ¡§on November 1994, take the way of ¡§Build-operate-transfer¡¨ to promote non-governmental circles participate in the investment. It is combine three parts of the government's power ,non-governmental fund and enterprise's administration efficiency to together, in order to finish the advance mode of the public works and moving up service quality, which create third of the win for government , people and enterprise's. BOT special project usually has such characteristics as long payoff period , investment amount is huge , risks involved are many and complicated, and BOT benefit which have more high uncertainty , market requirement that forecasting difficult question, so BOT is planning at the beginning, must consider when BOT plans to running , should pay attention to those can influence original financial revenue and expenditure parameter of planning, and austerely control on tolerate range, or the situation is not well how to do financial operation. This research just do the Financial Feasibility Analysis, and take Garden of Culture and Sports of Taipei (The Taipei Dome Complex of Song Shan Tobacco Factory) as a study case. This research approach is as following: 1. Documents retrospect 2. Case study 3. Make Financial Analysis with the Net Present Value Method 4. Every chapter is arranged as follows: Chapter two probe into documents and case preview Chapter three Financial assessment model and parameter setting Chapter four Financial cost and income Chapter five Cash-Flow Analysis Chapter six Self-Liquidating Ratio Analysis Chapter seven Benefit analysis of investment Chapter eight Financing feasibility analysis Chapter nine Sensitiveness analysis Chapter ten Conclusion and suggestion The limited of this research is: 1. On case study, there are not more clear materials of type for operation, so the type of operation is supposing in order to rent out. So as to ensure the stability of incomes, it is easy to do the feasible assessment of the financial. 2. The space of this case is not to make a verdict yet, so all assumptions of this research are in order to use the government of Taipei to announce while the meeting for leasing that the materials are quoted , consulted in the general quotations of market and the similar case for financial materials of The Kaohsjung Dome . So on practical working in this case , should calculate on agreement materials of the three sides - ' government unit , financing unit , folk unit '.