我國於2015年底通過新修正之個人資料保護法（以下簡稱「個資法」），將病歷納入特種個人資料中保護。目前個資法第六條特種個人資料列舉包含病歷、醫療、基因、性生活、健康檢查及犯罪前科之個人資料。雖然該條文係取法自國際賦予敏感性個人資料特別保護的模式，惟在個人相關健康資料保護部分，我國個資法不若歐盟一般資料保護規則（EU General Data Protection Regulation, GDPR）保護寬廣，納入資料之類型仍較國際立法例狹窄。尤其此次GDPR修法擴大特種個人資料空間，增列基因資料、生物性資料和性傾向，檢視我國特種個人資料列舉類型是否符合現今科技社會需求有其必要性。 過去研究針對健康資料個資法適用問題較少。大數據資料來源來自各處，以一般健康保健物聯網模式為例，自行操作之檢查數據或穿戴式裝置所蒐集之資料，若非須由醫師或其他之醫事人員施以檢查，而可由一般民眾自行測量之行為，該民眾自行測量之結果應不屬於個資法所謂之病歷、醫療或健康檢查個人資料，即非為特種個人資料。 惟大數據分析技術進步之環境下，健康資料亦攸關資料主體生理健康之敏感性，且容易連結並識別個人，考量健康資料敏感性提升，蒐集、處理、利用健康資料易侵犯到個人隱私，因此有加強保護之需求。將來可刪除個資法第六條第一項各種個人資料例示之「醫療」、「病歷」與「健康」資料，並新增「健康」或「與健康相關」之列舉項目。 但解釋「與健康相關」資料之內涵時不能無限上綱，在適用時應考量情境說，依據不同使用情境判斷是否為係作為特種個人資料利用，以排除一般性描述健康的使用情境。 / The change to the regulation of special categories of data (sensitive data) in the Taiwan Personal Information Protection Act (PIPA) in 2015 comes with the inclusion of medical records. The definition of sensitive data in the PIPA Article 6(1) refers to personal information of medical records, medical treatment, genetic information, sexual life, health examination and criminal records. However, the list of sensitive data in PIPA do not contain categories as broad as foreign legislation such as EU General Data Protection Regulation (GDPR). It is important to review the continuing relevance of existing categories of sensitive data in the light of change in social structures and advances in technology. Differ from “medical data” such as medical records, medical treatment and health examination, the collection, process and use of “health data” which is measured from wearable device, is not included in the sensitive data. Concerning the development of big data analysis, the “health data” which sensitivity enhanced is easy to identify an individual. It needs to give a higher level of protection to “health data” under PIPA. Therefore, this thesis suggests that medical records, medical treatment and health examination in PIPA Article 6(1) should be consolidated and amended to health records or data concerning health. However, this is not to say that the processing of all kinds of medical and health data should be regarded as the processing of sensitive data. But data, under certain contexts/circumstances may be treated as the processing of sensitive data.

本計劃因應台灣地區大量適婚年齡男女未嫁娶與少子化，所以許多人將寵物視為家庭的一份子，讓台灣寵物市場穩定成長，因此計劃打造一個以寵物為主題的行動平台，本平台將建立一個結合交友、社群與電子商務的綜合產業生態圈。 本計劃規劃的執行方式為藉由App的方式，讓會員免費使用交友與社團服務，建立以寵物為共同興趣的大型社群；再透過大數據與LBS機制（Location-Based Service基於位置的服務）進行寵物用品與食品相關的電子商務與廣告媒合。 本計劃預期效益為三年內產生8千萬元台幣營收與締造會員30萬人；針對個人方面，本計劃為對寵物有興趣的男女進行交友媒合，並為其建立實體與線上的交流社團；針對廠商方面，本計劃為大型寵物食品用品廠商建立行銷廣告通路，為小型寵物食品用品與文創商品廠商建立銷售通路。在商家方面，本計劃為寵物用品店、寵物醫院、寵物旅館、寵物美容院、寵物餐廳等等建立與客戶連絡與溝通管道。

近年來由於溫室效應加劇以及氣候變遷加劇，因此符合綠色運輸特性的公共自行車系統，成為各國交通部門發展綠運輸政策時的目標之一，同時，大數據分析亦是目前受到高度關注的熱門議題。而本研究首先使用臺北市微笑單車租借大數據探討在不同時間點下民眾日常使用微笑單車之旅運行為，分析不同站點間的旅次特性。再運用社群網絡分析，以站點之間旅次連結多寡作為權重，探討站點間之緊密程度，以及不同時間點下微笑單車租借量之熱點分布情形，並將其視覺化呈現。 後續透過文獻分析，擷取影響公共自行車使用量之因素後，本研究嘗試運用一般線性迴歸模型與地理加權迴歸進行模型建立，並探討各影響因素對於旅運需求之影響情形。實證結果顯示，地理加權迴歸模型可以解決一般線性迴歸所產生空間自相關問題，使得模型解釋能力獲得改善。本研究並使用地理加權迴歸進行使用需求分析以及預測，對未來公共自行車營運以及站點擴張提出結論以及建議，期能提升公共自行車系統之使用量。 / Due to the climate change and aggravation of the greenhouse effect in recent years, the public bicycle system with the feature of low-carbon emission has raised more and more attention internationally, and has become one of the targets in developing green transportation policies of transportation departments of governments around the world. Meanwhile Big Data analysis issues, on the other hand, are currently a sought-after topic which has caused great concern as well. In this study, we utilize the rental data of the YouBike system in Taipei to discuss the public usage of YouBike tour at different periods. With the use of social network analysis, we discuss the relationships between different bicycle stops based on applying the number of travels between different sites as the weight. Eventually, the hotspot analysis will be carried out by operating the GIS system. In this way, we are able to discuss the hotspot distribution of YouBike rentals in different time and then visualize the result. After that this study pick up the variables which will effect the YouBike usage by reference review. This research try to built models by utilizing the Least Squares Method and Geographically Weighted Regression. Then we will have a discussion with the result of the two models. The result shows that Geographically Weighted Regression can resolve the spatial autocorrelation problem which happened in the Least Squares Method and to gain a better result. With the analysis and prediction of public bicycle system from Geographically Weighted Regression, we hope to raise the usage of public bicycle system by concluding as well as making recommendations for the future operation of public bicycle and the expansion of bicycle stops.

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.

本研究針對企業赴海外上市決策面的三個構面進行探討並以台商為例，首先是探討企業赴海外上市前的動機為何，其次是企業如何選擇海外上市的地點，最後探討這些企業赴海外上市後的效益，也就是企業在海外上市的前、中、後三個不同階段所可能遭遇到的問題進行實務的探討，並以多重個案分析與類型比對的方式，以及一些台商上市現況的統計數字，希望以過去文獻中曾探討的議題來加以研究。 由於過去的研究偏重於台灣、香港、中國大陸三地市場，為了將層面擴大，特別從台商目前赴海外投資最最多的前十名的國家當中，挑出較為重要的六個國家，分別是香港、中國大陸、新加坡、美國、越南、泰國，並研究當地的已上市台商的現況，從當地上市台商所屬產業家數最多者，挑出具代表性的公司，進行個案研究及比較分析。 從個案研究發現，企業赴海外上市的動機，結果是“有利於開拓海外市場”及“易招募優秀人才”的營運面動機較多；企業海外上市地點的選擇，多半因為“市場流動性及規模及本益比”及“資金運用限制”；至於企業赴海外上市後的效益，結果是“提高公司聲譽”及“有助於產品銷售”另外，並發現香港及大陸上市後股東財富增加顯著。 最後，建議企業未來在選擇海外上市地點時，應多方面考量及評估，並非單純僅就幾個因素就決定企業海外的上市地點，應從各利害關係人的角度，去仔細思考在何處上市將是對企業最有利的。 / The research discusses three perspectives on the decision of enterprise go listing abroad and takes Taiwan enterprise as an example. Firstly, to study what are the motives of enterprises go listing abroad. Secondly, to research how enterprises choose listing place abroad. Finally, to discuss the benefits of these enterprises after go listing abroad. That is to say, to discuss the practiced issue that enterprise might encounter with the problems in the three different stages which before, in, after they go listing abroad, and analyze by way of pattern match, and the statistics of present status of some listed Taiwan enterprises with multiple cases, hope to research on the subjects has been discussed in the literature in the past. Because similar researches emphasize three stock markets in Taiwan, Hong Kong and Mainland China in the past, in order to expand the scope, especially from the top ten area invested by Taiwan enterprise at present, choosing six comparatively important area, including Hong Kong, Mainland China, Singapore, U.S.A., Vietnam, Thailand, and study the present situation of the already locally listed Taiwan enterprises, find the most ones from the local listed Taiwan enterprise's affiliated industry, choose the representative company, proceed with case study and comparative analysis. Find from case study, the motives of enterprises go listing abroad are " Contribute to exploit the overseas market " and " It is apt to recruit outstanding talents " the operation motives are more; the choices of enterprises’ overseas listing places, mostly because "Liquidity and scale and p/e ratio of stock market" and " Restriction on funding usage " ; and the benefits after listing abroad as for enterprises are " Improve company's reputation " and " Contribute to the product’s selling " In addition, the research shows shareholder's wealth increased apparently after listed in Hong Kong and Mainland China. Finally, the research suggests enterprises while choosing the listing place abroad in the future, it is considered and evaluated that should be in many aspects, not to determine the overseas listing place of enterprises alone simply rely on some factors, should think carefully in where to go listing is the most favorable to enterprise itself in terms of every interested party.