GitHub是2008年開始發展，提供線上源碼託管服務的網路平台。除了提供使用者建立組織、專案和存取軟體庫之外，更提供一些網站社交功能，包括允許使用者追蹤其他用戶、加入專案組織與關注軟體庫的動態，並且對於軟體源碼的修改和針對程式錯誤(bug)提出評論等，使用者或組織成員透過平台上版本控管服務來共同開發軟體專案，並透過GitHub提供的社交服務來完成溝通與協調。 本研究針對GitHub資料集進行整體性的觀察與分析，透過不同的社群網絡指標與分析方法，發現GitHub平台上的協同合作與社交活動。舉例來說，為了找出GitHub上網路的彈性，我們使用度分布，還有近距中間度及參與中間度的值。同時，我們針對使用者或專案之間的互動情形來分析關聯性，並以平台上不同的操作事件來觀察使用者是否偏好某些行為，抑或是某些事件之間是否會互相影響。 研究目標希望能透過GitHub平台所取得的部分資料，來推論GitHub上的真實情況。希望透過專案之間的關聯性，來找出平台上最具影響力的專案或使用者；也將針對程式語言與公司組織的關聯性，觀察技術之間的可替代性，與公司之間的相互合作的情況。同時以GitHub平台上不同操作事件之間的相關性，觀察出何種操作行為會影響使用者進行貢獻(提交源碼)。 另一方面，本研究將以專案的吸引力與黏著度等角度，來針對GitHub平台上的專案進行分析。針對這兩種維度進行觀察，期望進一步得知專案的貢獻程度，與專案隨著時間的所產生的變化。換言之，本研究方法將針對資料集中所有專案進行演進的推論，區分出專案演進的四個階段(活躍期、流動期、穩定期、衰退期)，並分析出目前GitHub上專案所處於的階段，最後研究出各階段轉換所可能的機率為何，進一步推論出專案未來演進的趨勢。最後，本研究提出了其他延伸之議題，例如重新定義專案演進階段、選擇適合的專案成員與專案的推薦等，以提供未來可行的研究方向。 / GitHub began to develop in 2008, providing an online open source hosting platform. In addition to providing user-created organizations, projects and software repositories, it also provides more social features, including allowing users to track other users, join the dynamic project or organization, watch software repositories, modify the source code for the software, and make comments for the program error (bug). In this study, we analyze of GitHub data sets; by using different network indicators and analysis methods in order to find collaboration and social activities on GitHub plat-form. For example, in order to find flexibility of networks on GitHub, we analyze degree distributions and values of closeness centrality as well as betweenness centrality. At the same time, we investigate the interaction between GitHub users and projects in order to analyze the correlation between them. On the other hand, we analyze attraction and adhesion of the projects on GitHub platform. By using these two indicators, we can get the degree of contribution of the pro-jects, and the changes of the projects over time. We consider the four stages of evolution (active, flow period, stable, recession) of the projects on GitHub. Finally, we study the probability of transition of the all stages, and further we infer the trend of the future evo-lution of the projects on GitHub. Finally, this study could be extended and used to support other studies. For example, we can redefine the evolution stage of a project, select members for the project, and rec-ommend the project.

近年來，隨著數位科技快速的發展，影像資料量迅速的增加，因此影像檢索成為重要的多媒體技術之一。在傳統的影像內容檢索技術中，使用影像低階特徵值，例如顏色(Color)、紋理(Texture)、形狀(Shape)等來描述影像的內容並進行圖片相似度的比對。然而，傳統的影像內容檢索僅提供單張影像查詢，很少研究多張影像的查詢。因此，本研究提出一個可針對多張影像查詢的方法以提供多張影像查詢的影像內容檢索。本研究將影像內容檢索結合社群網絡演算法，使用MPEG-7中相關特徵描述子和SIFT做為主要特徵向量，擷取影像的低階影像特徵，透過特徵相似度計算建立影像之間的網絡，並利用社群網絡演算法找出與多張查詢影像相似的影像。實驗結果顯示所提出的方法可精確的擷取到相似的影像。 / In recent years, with the faster and faster development of computer technology, the number of digital images is grown rapidly so that the Content-Based Image Retrieval has become one of important multimedia technologies. Much research has been done on Content-Based Image Retrieval. However, little research has been done on query by multiple images. This thesis investigates the mechanism for query by multiple images. First, MPEG-7 image features and SIFT are extracted from images. Then, we calculate the similarity of images to construct the proximity graph which represents the similarity structure between images. Last, processing of query by multiple images is achieved based on the social network algorithms. Experimental results indicate the proposed method provides high accuracy and precision.

近來Instagram成為流行的分享照片社交平台。在上傳影像到網路社交平台時，人們透過套用不同的濾鏡來表達他們的感受。然而，對於修改過的影像，我們不太可能逆向推回得知影像套用了什麼樣的濾鏡。本研究嘗試透過定義出十種影像風格，對應於一些最常應用的濾鏡，來解決這種逆向工程問題。因此，原始問題被轉化為分類問題，並可以使用機器學習方法來解決。為了生成訓練數據，我們根據用戶投票收集標記的結果。根據我們的實驗，在調查中概述的十個類別中，投票的結果有很高的共識。我們在HSV空間中使用分析出的顏色特徵來區分影像風格，並採用支持向量機（SVM）做分類。驗證我們數據集中的Top 1和Top 3準確度分別為64%和96%，顯示機器分類的效能與人類觀察者的效能相當。最後，我們導入數位著名攝影師的作品，進行個案研究，以測試風格識別和情感分析結果。 / Recently, Instagram has become a very popular social media platform for sharing photos. People apply different type of filters to express their feelings when posting photos on social networking sites. Given a filtered image, it is difficult, if not possible, to determine which filter has been applied to obtain the observed effects. This study attempts to address this reverse engineering problem by defining ten image styles corresponding to some of the most frequently applied filters. As such, the original question is cast into a classification problem which can be solved using machine learning approaches. To generate training data, we collected the labeled results based on user votes. Consensuses among users are found to be high in the ten categories outlined in our investigation. We employ color features in the HSV space to characterize image styles. Support vector machine (SVM) is then used for classification. The accuracies for top-1 and top-3 category using our dataset are 64% and 96%, respectively. The performance of machine classification is comparable to that of human observers. Finally, works by famous photographers are brought in to validate the style recognition and sentiment analysis results.

社群媒體伴隨行動載具的交互應用而蓬勃發展，傳統從網站首頁進入瀏覽或搜尋內容的使用經驗面臨嚴峻挑戰。越來越多網路使用者選擇直接在社群媒體訂閱、接收、和分享資訊，迫使內容傳遞或出版者必須通過經營社群渠道 ( 粉絲專頁 Fans Page ) 來為自身的平台贏得更多曝光與流量。 網絡的集體創作與分享特性，形塑了數位溝通的新樣貌。有別於傳統的高度組織化且單向遞輸內容的模式，社群渠道更注重與使用者之間扁平而多方的互動關係。藉由不同形式的語意語彙、視覺溝通、議題反饋、與時機頻率等交互操作，來提升使用者的參與 ( Engagement ) 和到達率 ( Reach )，對多數網路平台或品牌經營者而言，仍是高度重要之課題。 基於上述社群媒體經營的架構背景，本研究的重點項目，在於如何針對社群網站上的內容管理開發與優化互動成效，透過實際案例剖析與研究支持，尋求資訊傳遞與使用行為經驗之間存在滿足彼此需求的影響因子。 研究最終發現，在貼文形式與內容類別的變因下，對整體觸及和參與表現均有相當程度的影響，同時導致使用者的互動行為產生差異化。希冀本研究能對社群經營者勾勒出更明確的內容開發型態策略，進一步提升使用者的黏著度，並擴大傳播效益與廣度。

This study posits that there is a connection between political action online and activism in the real life. In addition, social ties and networks as well as political knowledge and efficacy play an important role in this connection. Causes, an application on Facebook, was selected for analyzed. A mixed method study was conducted, consisting of two stages. In the first stage (quantitative), a survey was utilized to investigate the correlation between activities on Causes and conventional political engagement. A total of 45 responses were gathered using convenience sampling. It was found that there is a strong correlation between action on Causes and conventional political engagement. For example, information retrieval activities are correlated with conventional activism (r=.418, p<.05), and social networking activities are also correlated with conventional activism (r=.661, p<.05). In addition to the survey, intensive interviews (N = 5) were conducted in the second stage (qualitative) to elaborate and clarify the results from the survey as well as to explore new grounds on the significance of ties and networks. Some themes have emerged from the interviews, including motivations for the use of Causes, Causes as an information channel, potential and problems of Causes, online versus offline activism, affiliation and involvement, political knowledge and efficacy as well as ties and networks. Interview findings concluded that the high correlation between online and offline activism is further specified by the interviewees to be an extension of each form of activism, meaning they are complementary rather than identical.

在政治權力結構中，權臣與派系在其政治人物的社會網絡中扮演重要的角色。本論文研究由職官年表中探勘權臣與派系。我們提出資料探勘演算法由職官年表中探勘循序共現樣式，以探勘出政府官員官職陞貶的共現關係。接著根據所探勘出的循序共現樣式，建立官員之間的社會網絡。透過社會網絡分析中的網絡中心性與社群偵測分別探勘出權臣與派系。本論文以清康熙時期的職官年表實驗驗證。透過視覺化分析顯示本論文所提出的方法有助於歷史學者的研究。 / In a power structure, chief officials and cliques play important roles in the social network and have high influence on politics. This thesis proposes an approach of social network mining from official chronologies to discover the chief officials and the cliques. We propose and develop the algorithm to discover the sequential co-occurrence patterns from official chronologies. Then the social network is constructed based on the discovered sequential co-occurrence patterns. Chief officials are discovered by network centrality analysis while cliques are discovered by community analysis of the constructed social network. The official chronology of Kangxi Emperor is taken as an example for experiments and the visualization analysis demonstrates that the proposed methods are helpful to assist historian for historical research.

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.