目的：在台灣，胰臟癌雖非癌症發生數最高，但85%為末期，五年存活率小於5%，有癌王之稱。如何早期偵測胰臟癌風險，一直是醫療上的重大議題。本研究運用時間矩陣搭配深度學習進行大量變數之胰臟癌風險偵測。 方法：健保資料庫收集國人疾病、用藥等結構化資料，可藉此反映每人健康狀態。將三年內診斷與藥物碼轉為時間矩陣，以卷積神經網路訓練，訓練組與測試組比例為9:1。 結果：共計案例組1095名及對照組10950名，訓練後測試組表現之AUROC(area under the receiver operating characteristiccurve)為0.937，六十歲以上及以下AUROC分別為0.846及0.897。 結論：結論：能將診斷、藥物、時間轉成矩陣，是以疾病軌跡預測胰臟癌風險，且能找到新特徵，未來搭配健康存摺，為低成本、快速、大量的胰臟癌數位快篩。

本研究希冀提供學生另一種程式的學習方式，減少學生對於程式的排斥感，同時亦能讓學生在學寫程式之前，先訓練其溝通能力，使其說話較具邏輯性的結構，如此一來將能更有效的表達其所要陳述的事件，我們自行開發一套軟體系統，可經由口語的方式與電腦溝通，同時電腦經過關鍵詞的辨識後，能更精準的透過推薦演算推出幾種可能的目標事件，透過選擇的方式，一步步引導使用者完成其想要達到的動作，最終目標是透過這個軟體自動產生程式碼，如此一來將可減少學生對於程式學習的恐慌，提高學生對於程式設計的學習成就，同時，請老師利用我們自行開發的程式學習輔助軟體實際教學後進行問卷調查，亦有超過百分之64的同學認為，這樣的軟體使他們對於程式學習更感興趣，也提升他們撰寫程式的成功率。

許多人在駕駛交通工具時常會速度太快，不放慢速度便會使車禍發生機率大幅提升，在巷弄裡更是如此。許多車禍都是因未注意來車所導致，所以我們想出了在路口裝設感知器，用來感知是否即將有車輛靠近以用來提醒駕駛人，雖然在某些路口會裝設凸面鏡，但還是有駕駛人會忽略，所以我們利用感知器搭配手機APP，當車輛靠近路口且有來車接近，手機便會發出聲響與震動，來提醒駕駛，相信這會比凸面鏡更有效果。

在這個資訊發達的時代，網路充滿著五花八門的資訊，導致我們在查詢資料時會因為這些雜亂且未經過濾的資料浪費許多時間，其中最為氾濫的便是點擊誘餌（clickbait），此種新聞常常有著吸引人的標題，而內容卻不會與主題相符，人們也常常在讀完整篇文章後才意識到自己浪費了許多時間在無意義的資訊上面。解決此問題很常用的方法之一便是運用摘要演算法來讓讀者先對新聞有一個大概的理解，不過，雖然摘要演算法越來越普及，但產生出來的摘要仍會和人為判斷的結果有所差距，進而造成閱讀理解上的錯誤以及偏差，所以我們想要藉由這次研究，從一個嶄新的角度切入，探討摘要演算法和句型分析之間的關係，融合原本向量建構的方式以及語句結構的分析來測試摘要的準確度，並且由結果研發出一個可以產生出更為精確的主旨之摘要演算法，除此之外，我們也會融合實地調查以及搜集意見的方式來更進一步探討人們思模式與產生出的摘要之關聯性。

本研究針對學習圖論演算法的需求，設計一套使用者友善的繪圖軟體Graphene。Graphene繪圖程式除了提供高可讀性的繪製結果，作為輔助繪圖的工具外，也可直接輸入競賽題目的文字格式測試資料產生繪圖結果，並結合現有繪圖演算法，改善、優化樹與類樹圖的繪製結果。此外，也加入時間軸、自訂外觀、參數調整、匯出圖片等功能，幫助學習者理解圖論演算法，亦可幫助教師製作教材，有助於圖論演算法教學。 Graphene採用的繪圖演算法以force-directed graph drawing演算法為基礎，實作節點的分布。然而初始的節點分布會影響繪圖結果，因此我們利用biconnected component、block-cut tree等圖論結構對圖的繪製進行優化。首先找出圖的biconnected component及關節點，重新定義block-cut tree裡的block，接著利用radial tree的布局方式配置每個block，再套用force-directed graph drawing演算法，得到最後的布局結果。如此可以減少不同block之間的交錯，得到較佳的結果。

In this project, we evaluate the effectiveness of Random Shuffling in the Cross Lingual Information Retrieval (CLIR) process. We extended the monolingual Word2Vec model to a multilingual one via the random shuffling process. We then evaluate the cross-lingual word embeddings (CLE) in terms of retrieving parallel sentences, whereby the query sentence is in a source language and the parallel sentence is in some targeted language. Our experiments on three language pairs showed that models trained on a randomly shuffled dataset outperforms randomly initialized word embeddings substantially despite its simplicity. We also explored Smart Shuffling, a more sophisticated CLIR technique which makes use of word alignment and bilingual dictionaries to guide the shuffling process, making preliminary comparisons between the two. Due to the complexity of the implementation and unavailability of open source codes, we defer experimental comparisons to future work.

情緒辨識是增進人際溝通的重要能力。如生命線、電話客服等應用情境缺乏表情、肢體語言等輔助時，單以語音進行情緒辨識有極高的實用價值。 本研究探討比較支持向量機（SVM）及卷積神經網路（CNN）兩種機器學習方法於訓練「AI語音情緒辨識」分類器模型的表現。我們採用SAVEE和RAVDESS兩個英文語音資料庫，並自行製作與標註「逼逼中文情緒語料庫」。研究結果顯示SVM對SAVEE資料庫單一情緒的辨識正確率達84~94%，個別錄音員正確率達75%，超越官網紀錄的73.7%。同時，實驗顯示深度學習的模型在訓練資料不足的狀況下，反而相對遜色。

本研究乃是以桌上型電腦塗抺散熱膏、風扇與防塵等研究，透過散熱與防塵策略，以達到其節能效果。在散熱膏研究中發現以0.2克五點降溫效果最佳；在風扇研究中發現以「前進風與後抽風」降溫效果更佳。在不同材質的過濾棉的研究中發現以「活性碳口罩」過濾細懸浮微粒效果最佳，並以活性碳口罩「最外層」的過濾效果更佳。在總結性評估中發現，散熱膏0.2克塗佈5點且加裝兩顆風扇「前進風與後抽風」與活性碳口罩「最外層」時，其降溫效果最佳。在總結性評估時測量電腦耗能，其耗電量約為0.01度，並無增加耗能，最後電腦內部積塵厚度為1mm，耗電量約為0.05度；積塵厚度為0.1mm，耗電量約為0.01度，能有效地節能。最後研發出磁吸式口罩架，隨時為電腦戴上防塵口罩。

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

本研究使用機器學習方法，改善年長者使用手機時觸控系統對於點按位置判斷之能力。首先設計實驗比較年長者使用手機時，點按位置及手勢判斷的準確率，接著收集年長使用者的觸控軌跡及裝置相關資料，並訓練模型以減少系統判斷的錯誤率和誤差幅度。再比較及分析不同機器學習模型對於本研究之資料的適用程度及經校準後點按位置準確率的提升，進而挑選出一個能夠最有效提升點按位置準確率的模型進行點按位置的預測。實驗過後選擇最有效提升準確率的Random Forest Regressor進行其他的校正實驗及分析。使用者點按位置的預測準確率能被有效提升，準確率能提高32.3%。而最終，將訓練後的模型套回實驗用的手機程式，系統判斷受測者的點按位置能從原本的63.7%提升至97.5%。

本研究利用蠟燭燃燒產生的碳焦，混加蘆薈及自製史萊姆開發為綠色產品~薈碳拉膜。研究中為取得碳焦，探討不同火源、吸附材質及離火焰高度對碳焦產生的影響，發現距離蠟燭火焰高度1公分處，以不鏽鋼杯底收集，可取得最多碳焦量。並以自組各種工具測試薈碳拉膜的延展性、隔熱及耐酸雨濕沉降等功能及運用電腦課所學編寫程式-程小奔進行RGB透光測試遮光效能。研究試驗出蘆薈碳焦拉膜的最佳組成比例為蘆薈：碳焦：自製史萊姆(20：0.6：10)。經與市面隔熱紙比較，蘆薈碳焦拉膜具有較佳展延性，防護酸雨濕沉降功能、遮光及隔熱能力佳，有取代化學隔熱紙的可能性，符合綠色化學節能、低毒、簡潔、可解原則，是值得推廣的綠色優質產品。