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.

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。

目前網路上流傳許多使用人工智慧修復照片的網站或應用軟體。然而，由於這些訓練資料多數來自國外，導致修復台灣「本地」照片的效果欠佳。此外，許多老舊照片因氧化、潮濕而泛黃，使得修復程序比起修復純黑白相片更加困難。因此，本研究旨在建立一個專門修復本地照片的機器學習模型，主要分為以下三個部分：首先，使用機器學習模型對老舊照片進行修復，包括著色、去模糊化和降噪；其次，分析使用不同比例之有色調照片(模擬泛黃照片)訓練模型的效果；最後，研究不同的修復順序(著色、去模糊化、降噪)和模型執行次數對照片修復效果的影響，發現「著色、去噪、去模糊化」的順序修復效果最佳。

現今的大型活動，如：校慶活動、新北耶誕城等，缺乏互動性與參與感，其中原因大多是觀眾時常埋沒於手機中的社交軟體當中所導致。而我們的研究將利用此特性，探討大眾對於活動的觀點，搭配網路爬蟲抓取使用者的貼文，觀眾只需在Instagram、Twitter等社交軟體中發布文章，系統就會即時推播至活動中的大螢幕上，並且結合圖像辨識快速審核貼文，設計出一套能改善互動性低落的解決方案。研究中我們探討不同的網路爬蟲演算法、圖像辨識技術，及問卷調查等來使作品更加精進，且搭配Line Bot、後臺管理，及常駐貼文等功能來為各類大型活動量身打造，也能夠運用於政令宣導或文宣廣告等用途上，大幅提升活動的互動性與精采程度。

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.

小提琴主要是由琴弓帶動琴弦使提琴透過音柱與音箱振動而發出聲音，而琴身、琴橋、琴弦、拉弦板、肩墊、松香……等，這些因素都可能影響提琴的「音色」，但是多數人，是很難用耳朵聽出「音色的差異」，因為聲音既看不到也摸不著，所以希望透過傅立葉電腦軟體Audacity，來分析聲音的『頻譜圖』，用科學化的證據，讓非音樂專業人士，也能「看」到音色的差異，並利用頻譜圖與聲響學理論說明音色的奧妙，讓喜歡音樂的我們，找到適合的小提琴，也讓初學者或演奏者更容易找到合適的琴。 在關於小提琴音色的實驗裡，影響小提琴音色表現最主要的因素是松香和琴弦。我希望將松香與琴弦所產生的音色用科學方法分析，建立資料庫，讓演奏者很快找到適合的松香或琴弦。

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.

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。

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.

本研究主要為進行安全帽改造，昔日我們常見關於汽車的盲區偵測、內輪差等先進技術皆建置於汽車上面，但實際在行車上造成災害發生的主角大部分為機車，我們閱讀相關文獻發現目前並無機車有相關技術，再者我們希望此技術可以讓騎乘機車、電動機車、電動腳踏車、腳踏車等對象皆可使用，因此我們於安全帽上裝設鏡頭及雷達感測器，透過鏡頭進行車輛物件識別，以識別車輛類型及輪距，進而以公式繪製內輪差曲線與進行盲區車輛偵測；透過雷達計算車側距離以判斷車輛是否會太靠近汽車或是落入汽車之內輪差範圍內，將以上偵測結果透過抬頭顯示方式直接投影在安全帽的面罩上，如此一來將可讓機車及腳踏車族在行車上更具安全性。

本研究提出有關數橋謎題遊戲的程式實作辦法，我們已知數橋被證明是個NP-complete問題。我們研製出了數種解題條件與窮舉策略在演算法上的實現，並且比較了這幾種解決辦法的相對效率，再者也提出了數個NP問題的解決策略。本研究發現在進行窮舉的時候以取最小頂點值期望效率最高(表示該程式能有相對多，相對快的執行結果)。