Recent studies showed that the probability of Taiwanese females developing breast cancer has risen dramatically over the past 30 years. We are now facing younger and more breast cancer patients in Taiwan. What makes the matter even more severe, is the fact that patients that take cancer treating medicine will suffer from its serious side effects, some may even lose the ability to reproduce. We hope to develop a new system that can help doctors and researchers develop new medicine for treating breast cancer, the way medicine cures cancer tumors are by attaching onto the infected cells’ receptors. After collecting MACCS data (converted from SMILES), the dataset will be used for training the machine learning program. Due to the problem of insufficient training data, we used an ensemble method to generate our machine learning model. Among the three basic ensemble techniques, Max Voting, Averaging, and Weighted Averaging. we selected the max voting technique to perform the prediction for this research. We created two separate datasets, positive and negative, the two datasets will later be used as training data for the program. We weren’t sure of the ratio of positive and negative in the training data, therefore we compare 40 different ratios and evaluate the results. By comparing the accuracy of the models, we found out that when the ratio between positive data and negative data is 1:3000, the machine learning program will have the highest precision. After we created the final model through voting among the 1000 models generated, we evaluate the precision of the model through the following methods, AUC, precision, recall. The ultimate goal of this research is to assist doctors and researchers shorten the process of developing and testing new medicines.

We study the creation of physical adversarial examples, which are robust to real-world transformations, using a limited number of queries to the target black-box neural networks. We observe that robust models tend to be especially susceptible to foreground manipulations, which motivates our novel Foreground attack. We demonstrate that gradient priors are a useful signal for black-box attacks and therefore introduce an improved version of the popular SimBA. We also propose an algorithm for transferable attacks that selects the most similar surrogates to the target model. Our black-box attacks outperform state-of-the-art approaches they are based on and support our belief that the concept of model similarity could be leveraged to build strong attacks in a limited-information setting.

「康威生命遊戲」是一款在1970年由劍橋大學數學家約翰•康威（John Conway）發明的單人遊戲，由棋盤構成的細胞組成，可以模擬邏輯閘元件，並且可以構建簡易計算器。 研究者在建構加法器和減法器的過程中，發覺手動放置電路元件非常耗時費力且容易出錯。為了解決這個問題，研究者決定開發一個能自動擺放元件的程式，以節省布局和放置元件所需的時間。參考了電子設計自動化(Electronic design automation)的概念，使用Python語言結合康威生命遊戲的應用接口模組，開發了一款自動電路布局程式。只要輸入邏輯電路的布林函式，就能自動設計、布局和放置元件，大大縮短了製作邏輯電路上的時間成本。因可直觀地展示訊號的傳遞和邏輯閘的運作，也有助於學生理解邏輯電路的運作原理，應用於教學。

第五代行動通訊（5th generation mobile networks）是現今科技發展的趨勢，新技術的出現也衍生出很多新的問題，在基地台點對點傳輸時，需要精確角度的天線才足以準確地接收高頻波短的毫米波，雖然現今已經有精密儀器能測量精確的方位角，但價格較高且使用方法複雜，面對數量龐大的5G基地台時，維修成本過高。本研究利用手機拍照得到天線與目標物相對角度，結合預先得知目標物的方位角，再經過數學運算即可得到精確的天線指向。本研究希望以隨手可得的手機，配合簡單的方法，可得到精確的天線指向，解決第五代行動通訊可能面臨的問題。

Max Gold's project, titled “Solving Mathematical and Chemical Equations using Python”, is a website comprising of 4 main programmes: one to find the smallest possible combination of two chemical compounds or elements; a self-made parsing function to convert a chemical equation into a matrix, then using Gaussian-Jordan elimination to find coefficients for an equation; a programme to parse a mathematical expression and use that parsed expression in algebraic division of an algebraic dividend of nth degree polynomial by a divisor of 1st degree polynomial; finally, a programme to solve binomial equations for the power s∈Q. This website was originally made so that Max Gold could improve his programming skills for GCSE computer science but expanded to incorporate his passion for chemistry and maths and thus allow others to use these programmes to help them with their problems as well. A problem with many conventional calculator websites is their lack of specificity – they tend to be able to compute some functions but not all. These programmes are tailored to GCSE and A level maths and chemistry, meaning this website provides an outlet to compute specific topics of problems.

Max Gold's project, titled “Solving Mathematical and Chemical Equations using Python”, is a website comprising of 4 main programmes: one to find the smallest possible combination of two chemical compounds or elements; a self-made parsing function to convert a chemical equation into a matrix, then using Gaussian-Jordan elimination to find coefficients for an equation; a programme to parse a mathematical expression and use that parsed expression in algebraic division of an algebraic dividend of nth degree polynomial by a divisor of 1st degree polynomial; finally, a programme to solve binomial equations for the power s∈Q. This website was originally made so that Max Gold could improve his programming skills for GCSE computer science but expanded to incorporate his passion for chemistry and maths and thus allow others to use these programmes to help them with their problems as well. A problem with many conventional calculator websites is their lack of specificity – they tend to be able to compute some functions but not all. These programmes are tailored to GCSE and A level maths and chemistry, meaning this website provides an outlet to compute specific topics of problems.

This work is devoted to solving the problem of orientation in the space of visually impaired people. Working on the project, a new way of transmitting visual information through an acoustic channel was invented. In addition, was developed the device, which uses distance sensors to analyze the situation around a user. Thanks to the invented algorithm of transformation of the information about the position of the obstacle into the sound of a certain tone and intensity, this device allows the user to transmit subject-spatial information in real time. Currently, the device should use a facette locator made of 36 ultrasonic locators grouped in 12 sectors by the azimuth and 3 spatial cones by the angle. Data obtained in such a way is converted into its own note according to the following pattern : the angle of the place corresponds to octave, the azimuth corresponds to the note and the distance corresponds to the volume. The choice of the notes is not unambiguous. However, we used them for the reason that over the centuries, notes have had a felicitous way of layout on the frequency range and on the logarithmic scale. Therefore, the appearance of a new note in the total signal will not be muffled by a combination of other notes. Consequently, a blind person, moving around the room with the help of the tone and volume of the sound signals, will be able to assess the presence and location of all dangerous obstacles. After theoretical substantiation of the hypothesis and analysis of the available information, we started the production of prototypes of the devices that would implement the idea of transmitting information via the acoustic channel.

本研究設計一個可以感測容量及感應開闔的垃圾桶，透過雲端接受各地垃圾桶的容量及使用狀態，回傳到中央戰情室統整訊息以編派人力進行回收，提高回收服務及效率。利用超音波模組感測到有人接近時驅動伺服馬達打開垃圾桶，以非接觸的方式讓使用者可以丟垃圾或回收物，另外於桶內放置超音波偵測容量，用紅藍綠三色LED顯示使用容量，桶滿的時候可控制不打開蓋子。垃圾桶的資訊會透過ESP8266 WiFi模組，利用MQTT通訊協定傳送到Node-Red建立即時的戰情室中央監控，用Line Bot發出即時訊息通知工作人員，整合的雲端物聯網系統，可以得知各個垃圾桶的狀況，達到減少人力成本及即時管理垃圾及回收物達到環境整潔並提高服務品質的目的。

本實驗最主要探討如何降低 Two Qubits System 經由 SWAP 閘後 Qubits 互相交換訊號時產生的誤差，用 Qiskit 環境測量雙量子位元系統誤差，利用隨機基準測量實驗找出誤差值，目前經由我們測量後經典量子SWAP閘的誤差值為2.003%。為了提高現代量子演算法的可信度及可行性，我們將經典量子SWAP閘的誤差定義為初始誤差，我們嘗試設計新的交換邏輯閘 SWAP 閘，將經典量子SWAP 閘替換成其他能達到相同目的的邏輯電路和調整脈衝作用時間，以及透過交叉共振理論以及調整脈衝圖形找出最優結構，延長超導量子電腦 ibmq_casablanca 的保真度，最後經由實際操作得到新的數值，並測量其誤差。我們成功的利用自製的SWAP閘降低 67.2%的誤差，也找出沒有相位翻轉也能實現的 SWAP 閘，這是個可行性很高的實驗。

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。

本研究主要是以Arduino為核心，製造出穩定度高且能自由開發各種智慧練球功能的桌球練習機。我從小喜歡動手拆裝機器和寫電腦程式，我的好朋友是桌球校隊，他希望擁有一台自己的桌球練習機，但是市售機器很貴，於是引發我的製作靈感。我以Arduino控制無刷馬達和伺服馬達閘門，使用 3D列印製作機身，再以手機藍牙App控制練習機，能發出不同速度的直球以及左右旋球，最後我加入腳架旋轉Arduino套件、人臉辨識及智慧練球模式（發遠身球及隨機出球功能）。穩定度的測試結果顯示：直球落點和左、右旋球皆穩定；選手測試心得都覺得新奇有趣。本研究的特點是首創智慧練球功能，兼具模組化、快速組裝、輕便、便宜、發球穩定的優點，可作為桌球校隊訓練設備。

現階段台灣有關多發性硬化症病灶分割相關研究付之闕如，但電腦視覺輔助醫療已日漸成熟。本研究新建構卷積神經網路，提出一套以深度學習為基礎，可自動辨識腦部核磁共振影像中，分割多發性硬化症病灶之人工智慧病灶判讀方法。本研究使用約翰霍普金斯大學影像分析與通訊技術實驗室授權提供之Longitudinal Multiple Sclerosis Lesion Imaging Archive資料集為訓練與測試資料，共151筆核磁共振臨床資料、39583744筆像素資料，並以IoU與dice係數為評估指標。經實證發現，本研究提出的方法，具有顯著IoU值，達0.8523，另dice係數亦較其他方法高，其值為0.9392，且在速度方面，禎數高達13.79。本研究成果未來可連結醫院之核磁共振影像資料庫，自動分割出核磁共振影像中多發性硬化症病灶，以利於早期診斷與治療。