本研究的目的在於探討Adversarial Voice Attack在對保護智慧財產權、抵抗自動翻譯的能力和預防智能設備危險，並以此進行大規模的應用。我們本次使用了常被用來做為翻譯系統的silero作為研究基礎和試驗對象，使得我們可以評估我們的對抗性樣本是否可以作為對抗自動翻譯的手段和它對模型的效果。利用FGSM(Fast Gradient Sign Method)方法生成對抗性noise來干擾模型對語音的辨識效果。我們的目標為透過解析模型label來製作能夠對模型進行有效攻擊的對抗性樣本，並以進化策略(Evolution Strategies)嘗試進行黑箱攻擊。 根據研究結果顯示在已知模型梯度的情況下可以做出噪聲極小的對抗性樣本。而我們在使用進化策略其中的協方差矩陣自適應進化策略(Covariance Matrix Adaptation Evolution Strategy)進行黑箱攻擊也可以使得製作出來的對抗性樣本的噪聲難以被人體感知。

鐵路時刻表排點直到目前為止仍十分仰賴人工作業，且排班優劣對於乘客服務品質有顯著的影響。本研究採用啟發式最佳化演算法以及模擬器進行旅客列車鐵路時刻表排點，希望能夠找出一份針對旅客需求，能夠提升旅客運輸成功率且降低旅途時間的時刻表。我們提出一種班表編碼機制，可依此機制產生班表草稿。我們研發的模擬器可將班表草稿轉換為合法無衝突之班表。最後，透過登山演算法來搜尋班表草稿，並以模擬器評估班表優劣，我們實現了一個自動化排班系統。實驗結果指出我們的模擬器能夠有效率地產生無衝突之班表，且所提出之演算法操作有助於提升運輸成功率和降低旅途時間。

在這次的研究中，我們在書上看到了一個問題，是一道有關於在棋盤上，貓和老鼠不能看到對方的問題。我們先研究這個題目中棋盤大小、貓和老鼠數量的規律，我們從1×1一路研究到了8×8，並且試著找出在不同棋盤大小的遊戲中，要有幾隻貓才能讓老鼠的平均數量接近2隻，之後我們將 題目設計成對戰的遊戲。 我們首先設計了一個棋盤大小是6×6的桌上型遊戲，並且修改過幾次規則。後來學習了程式設計，把遊戲改到電腦裡遊玩，我們使用scratch寫程式來製作遊戲，並且把原本6×6的棋盤擴大改成了8×8的棋盤。我們在試玩的過程中，又再次把一些不公平的遊戲規則修改了一下，最後我們和同學一起試玩遊戲，製作出了屬於我們的「貓鼠終極戰」。

此研究探討在正角柱及正角錐上一刀斬後分割成二部份而形成截面時，觀察其所形成的截面變化，並利用Geogebra、Desmos等電腦軟體模擬繪製，藉此來計算正角柱及正角錐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，以及其與側稜線長的關係。

培訓一位醫生的時間非常的久，除了在醫學院花上7~8年的時間學習之外，畢業取得住院醫師的身份後，還需要在醫院中選擇特定專業科目受訓，依照科別不同，受訓的時間從3~7年不等，其中神經外科醫生就需要花上7~8年的時間受訓，受訓後再通過專業醫師執照考試，才能正式成為醫師。 醫生的訓練過程費時耗力，在醫療訓練資源有限的情況下，採用虛擬實境(Virtual Reality,VR)科技，可以把手術訓練帶到虛擬世界中，不僅可以提供手術前開刀策略判斷，更可以提高醫療成功率。 本研究主要針對腦部腫瘤手術，藉由運用3D Slicer重建腦部腫瘤3D模型，並轉入到Unity製作手術模擬過程，再轉入到VR，讓醫師可以透過VR虛擬實境，進行手術練習與模擬。

Wibrazz is a wearable communication tool that allows the teacher, the therapist, the parent to communicate information to the child remotely using the device. Haptic (vibrationbased) feedback is becoming increasingly important in everyday life. A vibrating device that transmits information through clothing can help people with disabilities who have no or limited sensory use to live an integrated life in society without barriers.

隨著人工智慧和自然語言處理技術的發展，客戶端小型語言模型在各種應用中扮演著日益重要的角色，例如智能手機、物聯網設備和邊緣計算裝置等。這些小型 語言模型需要在有限的計算資源和存儲空間下實現高效的自然語言處理能力。在這種情況下，訓練資料量的大小對於客戶端小型語言模型的性能至關重要。 過去的研究已經表明，大規模的訓練資料對於建立高性能的語言模型至關重要， 但對於客戶端小型語言模型而言，資源的限制使得無法直接應用這些方法。因此，我們需要探討訓練資料量對於客戶端小型語言模型的影響，以找到最佳的平衡點。本研究的結果將有助於指導客戶端小型語言模型的設計和訓練，從而更好地滿足現實世界中的應用需求，同時充分利用有限的計算資源和存儲空間。

Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.

This report presents a novel AI-driven tool for aiding musical composition through the generation of chord progressions. Data acquisition and analysis are discussed, uncovering intriguing patterns in chord progressions across diverse musical genres and periods. We developed a range of deep learning models, from basic recurrent networks to sophisticated Transformer architectures, including conditional and style-based Transformers for improved controllability. Human evaluation indicates that, within the context of our specific data processing methods, the chord sequences generated by the more advanced models are practically indistinguishable from real sequences. The models are then integrated into a userfriendly open-source web application, making advanced music composition tools accessible to a broader audience.

In this fast-paced digital economy, customers' judgment is based on their experience with a company’s products and services. Customer reviews become a vital source of information for companies because this information can be used to enhance their products, understand customer wants and needs, improve brand reputation, and provide a competitor’s advantage. A company can understand customer needs and wants by going through reviews. Customers are encouraged to leave not only their opinion but also their ideas for the development of the product or service. By understanding these reviews, a company can actively respond and engage with a reviewer or problem. Failure of companies who don't answer customer queries may negatively impact customer loyalty. Customers will feel neglected by these companies and will choose competing companies to handle their needs. Additionally, customers may speak negatively about a company that does not respond to reviews. The AI-based customer sentiment dashboard can help gain a company's competitive advantage by identifying weaknesses in themselves and others. Companies will be enabled to understand where they succeed and where improvement is needed compared to their competitors, leveraging businesses to address strengths and weaknesses before competitors do. Through AI-based customer sentiment dashboards, a company can analyze its competitor’s reviews and use that information as leverage to make improvements to its products and services. Customers are increasingly leaving reviews on popular apps like Google Play, Stamped.io, Yapto, and Judge.me, Loox, Qualaroo, and Yelp. The reviews are rich in customer sentiments offering valuable insights into user satisfaction and pointing out the areas for improvement that are crucial to every company no matter how big or small. Despite their value, manually processing these reviews is a challenging task due to the large volume of unstructured data. Manual processing is also vulnerable to bias and human error, leading to inaccurate information. Traditional methods such as surveys have been proven to be ineffective if the main focus is targeted feedback and have low responses compared to reviews. The advances in artificial intelligence like Natural Language Processing (NLP) help us interpret and analyze human language and generate outputs like predicting what type of sentiments are in reviews. This project proposes developing an AI-based sentiment analysis model to evaluate customer feedback on two widely used taxi applications. Natural Language Processing libraries, such as the Valence Aware Dictionary and Sentiment Reasoner (. The model aims to categorize customer reviews into positive, negative, and neutral sentiments.

1.1 Short project summary My project involves the conceptualization and development of an innovative approach to modular self-assembling robotic systems. Through its ability to form any complex configuration, the system is highly adaptable to various scenarios and environments. Before delving deeper into the details of my project, I will provide an overview of my background and motivations. 1.2 Background Ever since I first watched the movie "Big Hero 6", I felt amazed by the applications of the so called “microbots”. From that point on, it made me always wonder what would be possible in the real world. When I did the research, I stumbled upon this field of modular robotics. Initially, I was unsure whether to embark on a project focused on electronics and robotics due to my background in programming. On the other side, this year gave me a chance to see the incredible performances of various projects at different science expos. Besides, I took part in the program of CANSAT LU and learned a lot during it, such as microchips, the control of miniature robotics, and the sensors of it. Finally, at school, I took the option Electronics where we dig into similar topics. With this accumulated knowledge and experience I felt confident enough to start this project.

現今醫療中，黃疸的早期偵測對肝臟疾病的預防與治療至關重要，但多數人難以在症狀輕微時察覺。我們希望藉由智慧手機影像結合機器學習進行黃疸檢測，提升民眾自我監測的能力。Su 等人（2021）曾使用深度學習和機器學習進行黃疸預測，但其方法依賴專業色卡進行色彩校正，成本高且限制應用範圍。本研究提出以白平衡演算法中的白色補丁法與灰界演算法，搭配深度學習模型 DCCNM1和2 取代色卡，提升黃疸檢測的普及性與便利性。經黃疸偵測效果評估顯示，DCCNM2 在無色卡模型中表現最佳，雖然各指標略低於色卡校正，但其展現出優異的穩定性和準確性，證明其作為無色卡黃疸篩檢方案的可行性。本方法將能提供便捷的居家黃疸檢測途徑，尤其對偏鄉地區居民而言，不僅提升早期發現的機會，還能有效減輕醫護人員的負擔，推動大眾健康管理。