智慧家庭環境是一個典型的分散式系統，在此類環境中的智慧服務大都由一至多個節點組成，例如一個智慧空調服務需要冷氣機、溫度感測器和邏輯判斷節點。然而，只要服務其中一個節點故障，整個服務就無法正常運作。由於居住在家庭中的大都是不具技術能力的使用者，故理想的智慧家庭服務，即使在有節點故障的狀況下，也應能在短時間內盡可能自動偵測與排除錯誤，使服務的運作不被中斷。本研究主要目的在於提出一個智慧家庭的強健服務管理系統，基於創新的複合式架構，結合集中式與非集中式錯誤偵測機制的特色，能在短時間內偵測到節點失效，進而恢復由於軟體所造成的節點故障或尋找待用節點，使得服務能繼續運行。 / Smart home systems are different from traditional computing systems. In a smart home system, a service is composed of several service nodes. For example, a smart air conditioning service needs a temperature sensor, an application logic, and an air conditioner. A service fails if one of its affiliating nodes fails. However, unexpected failures are undesirable for mission critical services such as healthcare or surveillance. Moreover, a smart home lacks professional system administrators. Users are generally unable to repair a service when it fails. Consequently, in a smart home system, the failed services have to be diagnosed and recovered automatically. In this paper describes a hybrid failure detection and recovery method for smart home environments. Experiments show that the proposed architecture is able to enhance overall availability of a smart home system in a short time.

資訊擷取是從自然語言文本中辨識出特定的主題或事件的描述，進而萃取出相關主題或事件元素中的對應資訊，再將其擷取之結果彙整至資料庫中，便能將自然語言文件轉換成結構化的核心資訊。然而資訊擷取技術的結果會有錯誤情況發生，若單只依靠人工檢查及更正錯誤的方式進行，將會是耗費大量人力及時間的工作。 在本研究論文中，我們提出字串圖形結構與字串特徵值兩種錯誤資料偵測方法。前者是透過圖形結構比對各資料內字元及字元間關聯，接著由公式計算出每筆資料的比對分數，藉由分數高低可判斷是否為錯誤資料；後者則是利用字串特徵值，來描述字串外表特徵，再透過SVM和C4.5機器學習分類方法歸納出決策樹，進而分類正確與錯誤二元資料。而此兩種偵測方法的差異在於前者隱含了圖學理論之節點位置與鄰點概念，直接比對原始字串內容；後者則是將原始字串轉換成特徵數值，進行分類等動作。 在實驗方面，我們以「總統府人事任免公報」之資訊擷取成果資料庫作為測試資料。實驗結果顯示，本研究所提出的錯誤偵測方法可以有效偵測出不合格的值組，不但能節省驗證資料所花費的成本，甚至可確保高資料品質的資訊擷取成果產出，促使資訊擷取技術更廣泛的實際應用。 / Given a targeted subject and a text collection, information extraction techniques provide the capability to populate a database in which each record entry is a subject instance documented in the text collection. However, even with the state-of-the-art IE techniques, IE task results are expected to contain errors. Manual error detection and correction are labor intensive and time consuming. This validation cost remains a major obstacle to actual deployment of practical IE applications with high validity requirement. In this paper, we propose string graph structure and string feature-based methods. The former takes advantage of graph structure to compare characters and the relation between characters. Next step, we count the corresponding score via formula, and then the scores are takes to estimate the data correctness. The latter uses string features to describe a certain characteristics of each string, after that decision tree is generated by the C4.5 and SVM machine learning algorithms. And then classify the data is valid or not. These two detection methods have the ability to describe the feature of data and verify the correctness further. The difference between these two methods is that, we deal with string of row data directly in the previous method. Besides, it indicates the concept of node position and neighbor node in graphic theory. By contrast, the row string was transformed into feature value, and then be classified in the latter method. In our experiments, we use IE task results of government personnel directives as test data. We conducted experiments to verify that effective detection of IE invalid values can be achieved by using the string graph structure and string feature-based methods. The contribution of our work is to reduce validation cost and enhance the quality of IE results, even provide both analytical and empirical evidences for supporting the effective enhancement of IE results usability as well.

現今解釋錯誤偵測系統及前扣帶皮質（ACC）的關係之理論主要為增強學習理論。增強學習理論認為個體會在行為後對於行為結果產生預期，並將該預期與實際結果進行比較，若實際結果較預期結果差則會活化ACC進而觀察到較大的FN（Feedback Negativity）振幅。近年來有學者提出奠基於增強學習理論的錯誤可能性理論，錯誤可能性理論則認為當個體在學習到行為與結果之間的關聯後，當接收到可能犯錯的訊息時便會活化ACC而引起較大的FN。本研究主要的目的為探討增強學習理論及錯誤可能性理論的適用性，其次為探討風險之因素是否能反映於FN上。由於兩理論對於風險情境中是否會觀察到FN有不同的預測，錯誤可能性理論預測會在高風險的情況下觀察到較大的FN；而增強學習理論則預測由於風險畫面並非回饋畫面，故風險不會影響FN。實驗一藉由探討風險與FN間之關係企圖提供兩理論初步之區分並提供風險研究的實驗證據，實驗一結果顯示FN確實會反映風險之因素。也點出增強學習理論用以解釋錯誤偵測系統之不完備之處。而實驗二則利用操弄回饋結果好壞及高低錯誤可能性以檢驗錯誤可能性對於錯誤偵測系統之必要性，實驗二結果顯示錯誤可能性為事件評估之因素之一。除此之外，實驗二亦提供FN反映懊悔之支持性證據。