電腦科學與資訊工程

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。

利用深度學習解決醫學問題一直是受矚目的研究主題。鑒於近期新冠肺炎疫情上升，有關新冠肺炎檢測的研究便成了熱門研究主題。目前，最有效的檢測方法是聚合酶連鎖反應 (PCR)，然而，PCR耗時甚久且有人為誤差。因此，以X光影像圖透過深度學習來診斷並分級是一個有效率且安全的做法。在研究中，我們利用深度學習進行疾病診斷，在五元分類上有相當高的準確率(84.91%)、在COVID-19單獨辨識時得到了極高的準確率(99.35%)、產生出疾病熱區及設計了新的分級系統( X-ray Severity Grading System , XSGS)，並將其用於嚴重程度分類，在不同分級下具有可辨別的差異。

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.

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。

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%。

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。

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）。）

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.