現代民眾獲取新聞的途徑逐漸轉移到網路媒體，然而在網路資訊快速傳播以及媒體為追求報導曝光度以增加金錢利益的情形下，片面、誘導等形式的新聞標題與短句訊息在新聞媒體傳播中日益嚴重；本次研究透過Fake News Challenge提供的Stance Detection dataset，運用深度學習與遷移學習方法訓練可預測兩文本之間相關程度的自然語言處理模型，在過程中改善調參及訓練方式，並將其實際運用在預測美國新聞媒體於Facebook網路社群平台發文推播新聞的同時所附的短句與新聞報導文本內容之間的相關關係程度，分析社群平台中新聞可能造成的誤導式文句是否實際造成片面報導，而影響了受眾對於媒體的使用程度與信任程度。使此模型有助即時預警社群平台上的報導資訊型態品質，輔助使用者獲取新聞時所應具備的媒體識讀能力，進而改善片面報導於網路的流竄，同時提升未來媒體生態。

手語為聾啞人士日常溝通的工具，但對一般人來說這是一種難以理解的溝通方式。本實驗使用深度學習的 Yolov3 與 Yolov4 模型訓練37個國語注音符號手勢，然後再驗證模型對圖片、影片、即時(Real time)攝影辨識的正確率。 實驗結果顯示：Yolo v3 圖片辨識度效果還不錯，但影片辨識度很差，而Yolo v4 不管在靜態的圖片或動態影片都有不錯的辨識率，另外在即時的影像辨識也有不錯的效果。 雖然有部分符號的辨識度很低，但這可能是訓練時照片拍攝的問題，如果可以改進拍攝的數量和技巧，相信可以大幅提升判讀的準確率。

With the introduction of the transformer architecture by Vaswani et al. (2017), contemporary Text Generation Models (TGMs) have shown incredible capabilities in generating neural text that, for humans, is nearly indistinguishable from human text (Radford et al., 2019; Zellers et al., 2019; Keskar et al., 2019). Although TGMs have many potential positive uses in writing, entertainment and software development (Solaiman et al., 2019), there is also a significant threat of these models being misused by malicious actors to generate fake news (Uchendu et al., 2020; Zellers et al., 2019), fake product reviews (Adelani et al., 2020), or extremist content (McGuffie & Newhouse, 2020). TGMs like GPT-2 generate text based on a given prompt, which limits the degree of control over the topic and sentiment of the neural text (Radford et al., 2019). However, other TGMs like GROVER and CTRL allow for greater control of the content and style of generated text, which increases its potential for misuse by malicious actors (Zellers et al., 2019; Keskar et al., 2019). Additionally, many state-of-the-art pre-trained TGMs are available freely online and can be deployed by low-skilled individuals with minimal resources (Solaiman et al., 2019). There is therefore an immediate and substantial need to develop methods that can detect misuse of TGMs on vulnerable platforms like social media or e-commerce websites. Several methods have been explored in detecting neural text. Gehrmann et al. (2019) developed the GLTR tool which highlights distributional differences in GPT-2 generated text and human text, and assists humans in identifying a piece of neural text. The other approach is to formulate the problem as a classification task to distinguish between neural text and human text and train a classifier model (henceforth a ‘detector’). Simple linear classifiers on TF-IDF vectors or topology of attention maps have also achieved moderate performance (Solaiman et al., 2019; Kushnareva et al., 2021). Zellers et al. (2019) propose a detector of GROVER generated text based on a linear classifier on top of the GROVER model and argue that the best TGMs are also the best detectors. However, later results by Uchendu et al. (2020) and Solaiman et al. (2019) show that this claim does not hold true for all TGMs. Consistent through most research thus far is that fine-tuning the BERT or RoBERTa language model for the detection task achieves state-of-the-art performance (Radford et al., 2019; Uchendu et al., 2020; Adelani et al., 2020; Fagni et al., 2021). I will therefore be focussing on attacks against a fine-tuned RoBERTa model. Although extensive research has been conducted on detecting generated text, there is a significant lack of research in adversarial attacks against such detectors (Jawahar et al., 2020). However, the present research that does exist preliminarily suggests that neural text detectors are not robust, meaning that the output can change drastically even for small changes in the text input and thus that these detectors are vulnerable to adversarial attacks (Wolff, 2020). In this paper, I extend on Wolff’s (2020) work on adversarial attacks on neural text detectors by proposing a series of attacks designed to counter detectors as well as an algorithm to optimally select for these attacks without compromising on the fluency of generated text. I do this with reference to a fine-tuned RoBERTa detector and on two datasets: (1) the GPT-2 WebText dataset (Radford et al., 2019) and (2) the Tweepfake dataset (Fagni et al., 2021). Additionally, I experiment with possible defences against these attacks, including (1) using count-based features, (2) stylometric features and (3) adversarial training.

本研究旨在運用自然語言處理技術，建立辨識惡意程式的模型。首先搜集良性及惡意執行檔，進行反組譯及前處理以建立資料集。使用反組譯後的組合語言檔作為文本，訓練模型以區分良性和惡意程式。研究比較詞袋模型、序列模型、fastText以及不同n-gram對模型的影響，並將結果與其他相似研究比較。 研究結果顯示。詞袋模型以使用multi-hot編碼表現最佳，序列模型有位置編碼的Transformer encoder表現最優。在不同n-gram的比較，2-gram詞袋模型識別惡意程式達到99.6%的F1-score，且本研究的識別準確率優於其他相似研究。

Face pose estimation has many possible applications, ranging from driver attention measurement systems to applications in the metaverse, which this project will be focused on. Rather than using a more traditional landmark-to-pose method where the head pose is estimated via keypoints, our method trains a simple convolutional neural network, using the dataset 300W_LP, where the images are simply inputted into the network. The model is fitted with three fully connected layers that are linked to the each of the three Euler angles (yaw, pitch, and roll), alongside multiple loss functions, which improve the robustness of the network.

近年來的研究指出：第二型糖尿病病徵與胰島類澱粉蛋白(islet amyloid polypeptide，簡稱IAPP)之聚集有關。如欲探討IAPP聚集機制以治療病症，需經多樣、多元的實驗條件進行試驗，這無疑是件耗時、耗費資源的大工程！本研究是以電腦軟體進行理論計算分析，採用分子動力學模擬系統，探討不同種類或官能基的有機分子對於IAPP作用之影響，再進一步觀察分子動態、統計系統能量，歸納出分子間的交互作用，釐清可能影響實驗結果的重要變因，期能達到提昇實驗設計、減少耗材浪費的綠色化學願景。本研究結果發現：在純水或稀薄食鹽水中，IAPP構形皆傾向摺疊結構；且具較高極性基團或易產生立障效應的有機分子會拉伸IAPP結構，使其分子間相互靠近聚集，形成β-sheet纖維；具較低極性基團的有機分子，易進入IAPP turn內，破壞引發聚集現象的疏水核心，進而抑制IAPP分子的積聚。

此研究探討螞蟻在各正多面體按特定行進規則進行表面行走之最短路徑，以及按照特定的截面規則將正多面體(柏拉圖立體)一刀斬後分割成二部份，觀察其所形成的可能截面變化，並利用Geogebra等電腦軟體模擬繪製，藉此協助我們計算正多面體分割成的截面周長與面積，進而推導出其公式及觀察截面大小之變化關係。

QR Code是由黑白模組組成的二維數位條碼，掃描後可讀取儲存的訊息。受限於設計原理，QR Code使用二進位制儲存資料。增加模組數目可增加資料量，但若在條碼內塞進太多模組時，尺寸太小的模組將無法被掃描器讀取。此外，目前QR Code掃描器僅支援單張掃描，並無法應付同時多張條碼掃描的實務需求。 如能克服顏色辨識，理論上彩色二維條碼將能克服現行QR Code的限制，但市面上並無相關產品可供測試。因此本專題設計了一款10×10、具8顏色的"Colour Matrix"，並利用Raspberry Pi開發Colour Matrix在手持裝置上運作的軟硬體來進行實驗。此實驗成功利用機器學習演算法在Raspberry Pi上進行的顏色辨識。開發的程式在單張掃描上效能與使用pyzbar辨識QR Code相當；在多張掃描方面，使用pyzbar辨識QR Code的解碼成功率為3.1%，而本專題的方法將成功率提升至92.4%，擴增數位條碼的使用範圍，具商用價值。

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.

在程式專案開發中，偵測錯誤常為最耗時的環節，進而影響整個專案的開發時長。而現今雖有些許輔助開發者提升偵錯效能的工具，但這些工具也只能藉由提供編譯執行中的資訊讓開發者省去偵錯時的繁瑣步驟，仍須開發者自行評估每段程式碼的正確性。此研究透過程式段落分析與資訊檢索實現自動錯誤定位，在每個程式段落標記其成為臭蟲（bug）的可能性。在程式段落分析中，執行使用者之原始碼，並透過歸納最終結果為正確及錯誤之執行路徑差異分析出每個程式段落的可疑性。接著運用資訊檢索技術於資料庫中找尋相似之原始碼，並參考其偵錯結果優化現有之可疑性，形成最終之可疑性排名。此研究不只結合了上述兩種技術，更優化可疑程度之計算方法以及資訊檢索中的相似度比對機制，達到更完善的錯誤定位。（此指「臭蟲」非語法錯誤（Syntax Errors），而為邏輯錯誤（Logic Errors）。）

本研究聚焦於高中生在寡勝賽局中的真人實驗之特徵表現。研究共計132位參與者，以同時使用即時線上施測之方式進行實驗，研究者於雲端蒐集資料做立即提供勝負結果及歷史回饋。 研究結果如下： 一、 在真人實驗中，記憶長度、遊戲經驗、數學能力、策略改變率，皆未對勝率、浪費率造成顯著影響。 二、 在真人市場中，無法出現週期性、浪費率隨場次降低之現象；在時間序列上有趨中特性。即真人並非單純依照歷史資訊的推斷而進行策略選擇，且無法藉由經驗學習。 三、 在真人實驗中，無法觀察到平均收益隨場次增加而提升或浪費率隨場次增加而降低的現象，說明經驗無法使真人群體學習或成長。 四、 電腦代理人在部分增加記憶長度後，平均收益與原先呈現顯著差異，真人市場則無法達成。即資訊量多寡對真人的策略判斷與勝率沒有影響，真人對歷史資訊的取用並非如電腦選取策略表具有唯一性。 五、 無論在電腦模擬或真人實驗中，浪費率與平均收益皆呈現高度負相關，此與其定義亦有關聯。且浪費率為0％時，平均收益會趨近於50％。斜率接近-0.5。 六、 策略改變率並非以任何函數圖形影響勝率，說明了相信歷史與否不是唯一影響結果的因素。在青少年群體的真人實驗中，未發現歷史資料對勝率有顯著之助益。

有鑑於高中選修化學相當困難，因此我們製作了一款有助於提升化學能力的遊戲。玩家必須活用沉澱表及化學來通過遊戲。蒐集離子球、並且按照要求合成出沉澱物或是酸鹼，來擊潰敵軍和敵方堡壘。 該遊戲是藉由網頁去宣傳的，因此我們需要一個平台去發布此遊戲，選擇了netlify這個平台。其中我們前端使用了三大前端程式，HTML、CSS、JavaScript，後端則利用Google試算表搭配Google APP Script去收集遊戲數據。最後，我們再利用AI演算法中的類神經網路分析，透過交叉比對找到玩家最有可能的成長結果。