Make a good use of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資訊懶人包

I’’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World

為了解決Make a good use of的問題，作者Fisk, Selena 這樣論述：

Life in the 21st century dictates that you need to know your numbers and use them to improve your decision-making and enhance your impact. From organizing the home budget, tracking health, understanding social media metrics, to running multi-national, multi-million-dollar organizations, it is no

longer acceptable to claim ’but I’m not a numbers person’ and believe that it is someone else’s job. Data is everywhere. Smart watches track our steps, heart rate, and blood-oxygen levels, social media platforms recommend people we might know and products we might like, and map applications on our

phones suggest when we should leave home, taking into consideration where our next appointment is and what the traffic is like. Dr. Selena Fisk believes that the data-informed can use the numbers in conjunction with an understanding of contexts, people, and different situations, to lead change and

make shifts in what they do. This book steps through the ’why’ of data and the types of data we often see and use through three key areas: data literacy, data visualization, and data storytelling.

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照顧者對於育兒APP使用經驗及滿意度之研究

為了解決Make a good use of的問題，作者張可橙 這樣論述：

自2020年COVID-19疫情延燒至今，對家庭帶來很大的生活改變，其中除了育兒日常之外，在防疫期間家庭互動型態也正悄悄地改變。因此，為了解家長育兒實際需求以及使用相關資源是重要的趨勢。家有嬰幼兒的父母需要紀錄各種嬰幼兒的生活紀錄，以確保嬰幼兒的健康狀況及健康檢查，如何善用各項育兒資源，將嬰兒照護資訊化，家長可以即時了解子女目前的狀況。隨著資訊科技進步，智慧型手機的流行，數位工具也愈來愈行動化及便利性，因此針對嬰幼兒各項活動的APP也蓬勃發展。目前市場上育兒APP種類眾多，但深入探討實際使用與功能是否滿足照顧者需求的研究較少。為了解照顧者對於使用育兒APP相關經驗及滿意度為何？研究首先依據文

獻探討嬰幼兒相關文獻，了解行動裝置在嬰幼兒保育相關之領域應用，再將市面上手持行動裝置平台皆有上架的育兒APP，將各個的功能做比較與統整，以問卷調查方式了解照顧者對於育兒APP實際使用情形以及滿意度。本研究採用科技接受模式為研究架構，加入受試者背景變項探討各構面關係，利用SPSS統計分析方法來驗證各項研究假設。研究結果如下：探討照顧者對於育兒APP的使用經驗之現況與差異。「認知有用性」及「認知易用性」會影響「使用意願」；「使用意願」會影響「滿意度」。根據研究結論，提出相對應研究建議，供未來建置應用程式系統可以擴充功能參考，讓使用者滿意度更加提升。

Awkward Intelligence: Where AI Goes Wrong, Why It Matters, and What We Can Do about It

為了解決Make a good use of的問題，作者Zweig, Katharina A. 這樣論述：

An expert offers a guide to where we should use artificial intelligence--and where we should not.Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There’s a tendency to think that machines

can be more ＂objective＂ than humans--can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI’s supposedly objective

decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot--but it’s bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reignin

g champions in games of chess and Go. But, drawing on her own research, Zweig shows how inaccurate AI is, for example, at predicting whether someone with a previous conviction will become a repeat offender. It’s no better than simple guesswork, and yet it’s used to determine people’s futures. Zweig

introduces readers to the basics of AI and presents a toolkit for designing AI systems. She explains algorithms, big data, and computer intelligence, and how they relate to one another. Finally, she explores the ethics of AI and how we can shape the process. With Awkward Intelligence. Zweig equips u

s to confront the biggest question concerning AI: where we should use it--and where we should not. Katharina A. Zweig is Professor of Computer Science at the TU Kaiserslautern in Kaiserslautern, Germany.

採用CUDA圖型處理器平行化改良5G軟體基地台之隨機存取通道流程

為了解決Make a good use of的問題，作者王靖 這樣論述：

隨著5G逐漸於全球開始商轉，越來越多企業發現其中商機並相繼開發相關應用與服務，例如：無人機、物聯網、邊緣運算等，然而這些應用都需要基地台為其傳遞訊號才能正確運作，因此基地台本身的穩定與效能將是這一切的基礎。本論文即提出一改善方法以提升原基地台本身之運算效率使其能夠更穩定的提供服務。無線行動網路近年快速發展，於是有軟體化基地台（Software-defined Radio, SDR）的概念被提出並運行提供服務，此概念即透過編寫軟體程式提供傳統基地台之服務，以應付行動網路技術規格之快速發展與變遷。本論文在此基礎之上針對基地台中提供使用者註冊接入網路與使用者裝置同步服務的隨機存取通道（Random

Access Channel, RACH）流程，討論其傳統實作方法並提出一改善效率之方法與流程架構。本論文將研究使用圖型處理器（Graphics Processing Unit, GPU）加速平行RACH 流程上的運算，並修改運算流程與方法使之更適合運行於GPU。透過本論文提出的架構設計，基地台的模擬測試運算執行時間可調降至大約原本的10%～50%。本論文的架構亦提供彈性化設計，因此可一次處理多基地台接收之訊號，且由於本研究將所有運算拆開至不同運算單元上平行運算，所以即使需要處理的訊號增加，總處理時間也不會有太大的差異。藉此研究，軟體基地台運行時將能有更多閒餘的效能維持整體性之效能與穩定或是

提供更多服務應用。