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Exploring Graphs with Elixir: Connect Data with Native Graph Libraries and Graph Databases

為了解決query程式的問題，作者Hammond, Tony 這樣論述：

Data is everywhere - it’s just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and y

ou’ll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We’ll start from the basics and examine the main graph paradigms. Get ready to embrace the world of co

nnected data!Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and conc

urrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets.Using Elixir and graph-aware packages in the Elixir ecosystem, you’ll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for compar

ing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. T

ransform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions.Harness the outstanding power of concurrent processing in Elixi

r to work with distributed graph datasets and manage data at scale.What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is

generally required. Instructions for installing the graph databases are given in an appendix.

query程式進入發燒排行的影片

一個基於深度神經網路用以檢索手繪草圖的使用者介面影像之方法─以行動應用程式為例

為了解決query程式的問題，作者林思茗 這樣論述：

隨著當今互聯網的廣泛覆蓋及行動設備的使用越來越普遍，導致行動應用程式的開發與設計過程越發重要；在行動應用程式的設計過程中，為了加快UI設計師在搜尋相關應用程式UI設計案例以用來激發靈感，本文提出一種基於草圖檢索行動應用程式UI設計實例的方法，讓UI設計師只輸入根據初步想法隨手描繪出的草圖，即可搜尋到與輸入草圖相似的設計案例。本研究提出的方法由兩個模型組成，分別是基於CycleGAN的影像到影像轉換模型，以及基於卷積自編碼器的檢索模型。首先，我們使用影像到影像轉換模型，將複雜的UI設計案例截圖轉換為與UI設計草圖相似的結果，稱生成草圖；接著，我們使用檢索模型從輸入的手繪草圖及生成草圖(UI設計

案例截圖)中，提取特徵並將其轉換為低維的特徵向量，隨後，我們使用餘弦相似度來計算手繪草圖與生成草圖的特徵向量之間的相似值，且根據此值來排序及輸出與查詢案例最為接近的UI設計實例。為了能更好的驗證及評估我們系統的性能，我們將另外兩個系統作為基準線的方法比較；根據實驗結果，我們的方法明顯優於其他兩個基準線，其中，系統對於top-1查詢結果的準確率為72.83%，而對於top-5與top-10查詢結果的準確率分別達到85.51%和92.39%。此結果表明，我們提出的基於草圖檢索UI設計案例方法，在行動應用程式開發過程中實現的潛力頗大，因為它有助於UI設計師在搜尋設計案例的過程中，減少搜尋到無關的設計

案例的次數，從而節省工作時間並提高工作效率。

Exploring Microsoft Excel’s Hidden Treasures: Turbocharge your Excel proficiency with expert tips, automation techniques, and ov

為了解決query程式的問題，作者 這樣論述：

Discover timesaving features, accessibility and internal control approaches, data integrity improvements, and spreadsheet automation techniques by exploring Excel shortcuts and nuances in Microsoft 365, Excel versions till 2021Key Features: Get hands-on experience by carrying out techniques in de

tailed example workbooksReclaim portions of your day by immediately implementing data integrity and automation featuresIncorporate spreadsheet disaster recovery techniques into your daily workBook Description: David Ringstrom coined the phrase ＂Either you work Excel, or it works you!＂ after observin

g how many users carry out tasks inefficiently.In this book, you’ll learn how to get more done with less effort. This book will enable you to create resilient spreadsheets that are easy for others to use as well, while incorporating spreadsheet disaster preparedness techniques. The time-saving techn

iques covered in the book include creating custom shortcuts and icons to streamline repetitive tasks, as well as automating them with features such as Tables and Custom Views. You’ll see how Conditional Formatting enables you to apply colors, Cell icons, and other formatting on-demand as your data c

hanges. You’ll be empowered to protect the integrity of spreadsheets and increase usability by implementing internal controls, and understand how to solve problems with What-If Analysis features. In addition, you’ll master new features and functions such as XLOOKUP, Dynamic Array functions, LET and

LAMBDA, and Power Query, while learning how to leverage shortcuts and nuances in Excel.By the end of this book, you’ll have a broader awareness of how to avoid pitfalls in Excel. You’ll be empowered to work more effectively in Excel, having gained a deeper understanding of the frustrating oddities t

hat can arise daily in Excel.What You Will Learn: Explore hidden and overlooked features that will save your timeImplement disaster prevention and recovery techniquesImprove spreadsheet accessibility for all usersBolster data integrity and spreadsheet resilienceCraft code-free custom worksheet funct

ions with LAMBDACreate code-free report automation with Power QueryIntegrate spreadsheet automation techniques with easeWho this book is for: This book is for intermediate to advanced excel users working in diverse roles such as business users, accountants, project managers and business analysts amo

ng others. The more time that you spend in excel the more time this book will save you. You will be able to maximize your productivity by learning spreadsheet interactivity, accessibility and automation. This clear step-by-step explanation and detailed example workbook will help you to try out new t

echniques firsthand and leverage them for your business’s advantage in no time.

醫療物聯網產業技術發展與未來展望之研究

為了解決query程式的問題，作者李宜杰 這樣論述：

本文藉由研究文獻、產業資料及技術報告的內容，進行分析醫療物聯網的產業及技術現況，並進一步探討在全球新冠肺炎病毒疫情肆虐下，醫療物聯網扮演降低疫情衝擊之功能及角色。使用醫療物聯網作為健康監視系統，可以通過使用可穿戴健康監視設備、無線人體局域網、人工智慧和基於雲端遠程健康測試來提供即時監視。所已利用醫療物聯網功能組件（例如數據收集，存儲，傳輸和分析）來控制傳染病傳播的預警系統會很有幫助。本研究除了說明醫療物聯網的結構、生態系統、應用程式及穿戴裝置外，也介紹認知醫療物聯網及人本角度的醫療物聯網，並特別於第三章研究方法於實施，說明了「降低疫情衝擊的醫療物聯網結構」、「醫療物聯網中的人工智慧和大數據技

術」、「醫療物聯網的安全機制」、「各國（台灣、韓國、德國）因應新冠肺炎病毒上的醫療物聯網應用和技術」、「應用醫療物聯網降低新冠肺炎病毒疫情衝擊」。最後本研究說明了醫療物聯網未來的挑戰為「安全機制」、「安全有效的演算法」、「能源效率」、「交互操作、標準化及規格」、「隱私」及「信任」等潛在問題，以及對產業及後續研究提出建議。