Bug的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和整理懶人包

The Mongolian Death Worm: On the Hunt in the Gobi Desert: Or, How I Found the Worst Bathroom on Earth and Learned to Love Cheese

為了解決Bug的問題，作者Spain, Pat 這樣論述：

Pat Spain is not a very good dancer. Nor is he a person used to wearing bikini briefs, or wrestling in front of hundreds of nomads and an international TV audience. He is certainly not a person you would expect to find wearing said bikini briefs while dancing in front of said audience, but here w

e are: On the Hunt in the Gobi Desert. Pat and a National Geographic film crew are searching for the truth behind stories of the Mongolian Death Worm, and to crack this legend Pat will have to wrestle a giant while risking indecent exposure, brave the worlds’ most disgusting long-drop bathroom, eat

and drink toxic ’delicacies’, wrangle a very jumpy electric eel and testy spitting cobra, avoid the temptation to smuggle archeological artifacts and deal with bed-bug and camel-tick infestations while they traverse the least densely populated country in the world, Mongolia.

Bug進入發燒排行的影片

測試套件精簡對錯誤定位影響的實務探討

為了解決Bug的問題，作者龐之茵 這樣論述：

測試套件精簡是一種被頻繁用於提升回歸測試效率的方法，而頻譜式錯誤定位則是縮短除錯過程的有效方法。這兩種方法在文獻和實務上中都受到相當多的關注，並且可以整合到持續集成環境中。測試套件精簡的運作有三個重要因素(即測試套件精簡策略、評估測試個案的指標和覆蓋粒度等級)，連同頻譜式錯誤定位技術的選擇可視為持續集成環境的組態。由於不同測試套件精簡技術會導致回歸測試所執行的測試個案不同，並產生不同的測試結果，而頻譜式錯誤定位運作更是奠基於測試個案的執行紀錄與測試結果。因此，測試套件精簡技術和頻譜式錯誤定位技術的選擇都會對錯誤定位的成效和持續集成的效率(即持續集成週期的長度)產生影響。有鑑於此，軟體開發人員

在規劃持續集成的細節時，測試套件精簡改進技術的選擇與頻譜式錯誤定位技術的搭配變成一個複雜且讓人傷神的問題。特別是文獻中相關技術的數量皆相當驚人，這被認為是一項複雜的任務。本研究的目的是調查上述各個持續集成的組態參數如何影響錯誤定位的成效和持續集成的效率。此外，測試套件精簡會減少測試套件的大小，以致改變用於執行錯誤定位的測試頻譜。因此，本研究還分析測試套件大小對錯誤定位成效和持續集成效率的影響。本研究鎖定三個測試套件精簡戰略、四個評估測試個案的指標、三個覆蓋粒度等級和四個頻譜式錯誤定位技術，針對所形成的約150個組合進行分析，而實驗的進行則是基於九支受測程式的226個錯誤版本。實驗結果顯示，測試

套件精簡無助於提升錯誤定位的成效。若基於特定因素而必須在持續集成中執行測試套件精簡，建議採用的覆蓋粒度等級是分支覆蓋。相較於覆蓋粒度等級，其他兩個測試套件精簡的因素不會對錯誤定位的成效造成統計上的顯著差異。再者，實驗結果也顯示，無論軟體開發人員是否在持續集成中包含測試套件精簡，Jaccard皆展現優於其他頻譜式錯誤定位技術的成效。此外，雖然錯誤定位的成效與測試套件的大小呈負相關，但統計分析結果指出其相關性並不顯著；另一方面，實驗結果顯示知名的測試套件精簡策略HGS若搭配函數覆蓋最有助於優化持續集成的效率。至於評估測試個案的指標和頻譜式錯誤定位技術的選擇等兩個因素對持續集成的效率則無統計上的顯著

影響。最後，無論持續集成環境中是否納入測試套件精簡，持續集成的效率和測試套件的大小之間都存在不可忽略的正相關。

Dr. Grossology’s Book of Gross: Green Slime, Frog Brains, Bug Guts, and More Yucky Experiments and Facts

為了解決Bug的問題，作者Dr Grossology 這樣論述：

Yuck! Discover all things gross, nasty, disgusting, grimy, slimy, and vile with Dr. Grossology’s Book of Gross.From oozing snot to squirming bugs and smelly poo, Dr. Grossology has collected all of the grossest things imaginable in one nauseating book. Discover all of the gross phenomena in the w

orld with slimy, smelly, sticky, gruesome, and foul topics to turn your stomach. You’ll entertain your friends and disgust every adult around you. Did you know that the regal horned lizard squirts blood out of its eyes? Or that you produce nearly two gallons of snot each week? What about that there

are around 2,400 types of bacteria living in your belly button? And that lobsters pee out of their faces? Ick! This guide is irresistible to kids who love to gross you out! Inside you’ll find: - Loads of disgusting science experiments not for the faint of heart - Tips on how to throw a yucky party w

ith ear wax snacks, fake dog poo, fish slime, and more - Expert advice from Dr. Grossology on how to gross people out! Get tons of yucky information on every gross thing imaginable, from repulsive science facts and freaky nature to everything unsavory about the human body. Get ready to disgust all

your friends and family and make them say ＂ew!＂ with Dr. Grossology’s Book of Gross!

整合式深度學習模型用以改善臨床聽神經瘤自動圈註流程

為了解決Bug的問題，作者黃梓軒 這樣論述：

中文摘要 iAbstract iiTable of Contents iiiList of Figures vList of Tables viiiCHAPTER I. INTRODUCTION 11.1 Brain Tumor and Medical Imaging Analysis 11.2 Computer-aid diagnosis on medical images. 11.2.1 Using CNN-based semantic segmentation algorithms on medical images. 21.2.2

Using CNN-based object detection algorithms on medical images. 31.3 Combining AI and clinical knowledge for medical assistance 31.4 Semi-automatic tumor labeling system for physicians 5CHAPTER II. IMPROVING THE VOLUME EXTRACTION OF VESTIBULAR SCHWANNOMA USING LONGITUDINAL MRI AND TWO-STAGE

LEARNING. 72.1 Motivation 72.2 Materials: Longitudinal MR images of vestibular schwannoma for GKRS 72.3 Methods 82.3.1 Data pre-processing 82.3.2 Training procedure for the segmentation models. 102.3.3 Postprocessing and evaluation of segmentation model outputs 122.3.4 Statisti

cs for tumor volume predictions 122.4 Results 132.5 Discussion 162.5.1 Effects of two-stage training with different loss functions and data utilizations 162.5.2 Overestimation because of the partial volume effect 172.5.3 Postprocessing alignment correction 172.5.4 Limitations and c

onclusions 18CHAPTER III. DETECT VS LESIONS USING YOLOV2 WITH DIFFERENT DEPTH RESIDUAL NEURAL NETWORK AS BACKBONE MODEL. 193.1 Motivation 193.2 Methods 213.3 Results 243.4 Discussion 26CHAPTER IV. THE EFFECT OF MULTI-PARAMETRIC IMAGE AND RESOLUTION ON VS TUMOR DETECTION 284.1 Mo

tivation 284.2 Resizing multi-parametric MR images by cropping and interpolation 284.3 Detect VS lesions using Faster RCNN, YOLO-v2, and YOLO-v5. 37CHAPTER V. Detection and segmentation of Multi-types Brain tumors 385.1 Detecting and classifying five kinds of brain tumor lesions in MR im

ages using CNN 385.2 Brain tumor contouring from bounding boxes 40CHAPTER VI. LIMITATIONS AND FUTURE WORKS. 43Reference 44