A Model for Context-Aware Location Identity Preservation using Differential Privacy
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xxxxxxx公司ATYAQ–A02-2013安全标准化管理手册依据标准AQ/T9006-2010《企业安全生产标准化基本规范》受控状态:受控版本号:A/O-A02发放号:编制:审核:批准:2013-1-25发布2013-1-31实施xxxxxxxxxxxxxxxxxxxxxxx公司发布目录第一章总则第二章手册管理第三章各类安全管理制度第一部分基本安全生产管理制度A1.1安全目标管理制度A1.2安全生产目标管理责任制考核奖惩办法A2.1安全生产责任制A2.2安全生产责任制管理制度A2.3安全会议制度A3.1安全生产费用提取和使用管理制度A3.2工伤保险管理制度A4.1法律法规规范管理制度A4.5安全生产规章制度和操作规程管理制度A4.6文件和档案管理制度A5.1安全教育培训管理制度A5.3特种作业人员管理制度A6.1建设项目安全设施“三同时”管理制度A6.2生产设备设施安全管理制度A6.2施工和检(维)修安全管理制度A6.2生产设备设施维护保养安全管理制度A6.2生产设备设施验收安全管理制度A6.2生产设备设施报废安全管理制度A6.4特种设备安全管理制度A7.1“三违”行为检查管理制度A7.1作业安全管理制度A7.1现场带班安全管理制度A7.2消防安全管理制度A7.3警示标志和安全防护管理制度A7.4相关方安全管理制度A7.5变更管理制度A8.1安全检查及隐患治理管理制度A9.1危险物品和危险源管理制度A9.2风险评估和控制管理制度A10.1职业健康管理制度A10.2劳动防护用品(具)和保健品管理制度A11.1应急管理制度A12.1安全事故管理制度A13.1安全绩效评定管理制度A13.2岗位达标管理制度第二部分其它安全生产管理制度安全生产奖惩制度巡回检查制度用电管理制度交接班制度安全生产值班制度外来人员及车辆管理制度安全标准化运行自评制度动火作业安全管理规定高处作业安全管理规定动土作业安全管理规定施工作业安全管理规定临时用电、用气安全管理规定装卸、运输安全管理规定危险化学品安全管理规定应急救援器材维护制度第四章各类操作规程第五章安全教育第六章安全作业证第七章安全生产技术通则第八章应急救援预案第一章总则安全生产是企业的头等大事,必须坚持“安全第一,预防为主,综合治理”的方针和群防群治制度,认真贯彻落实《中华人民共和国安全生产法》,切实加强管理,保证职工在生产过程中的安全与健康。
计算机编程英语词汇计算机编程英语词汇在计算机编程中,经常要用到英语,那么有哪些计算机编程英语常用词汇呢?以下是小编整理的计算机编程英语词汇,欢迎阅读。
第一部分、计算机算法常用术语中英对照Data Structures 基本数据结构Dictionaries 字典Priority Queues 堆Graph Data Structures 图Set Data Structures 集合Kd-Trees 线段树Numerical Problems 数值问题Solving Linear Equations 线性方程组Bandwidth Reduction 带宽压缩Matrix Multiplication 矩阵乘法Determinants and Permanents 行列式Constrained and Unconstrained Optimization 最值问题Linear Programming 线性规划Random Number Generation 随机数生成Factoring and Primality Testing 因子分解/质数判定Arbitrary Precision Arithmetic 高精度计算Knapsack Problem 背包问题Discrete Fourier Transform 离散Fourier变换Combinatorial Problems 组合问题Sorting 排序Searching 查找Median and Selection 中位数Generating Permutations 排列生成Generating Subsets 子集生成Generating Partitions 划分生成Generating Graphs 图的生成Calendrical Calculations 日期Job Scheduling 工程安排Satisfiability 可满足性Graph Problems -- polynomial 图论-多项式算法Connected Components 连通分支Topological Sorting 拓扑排序Minimum Spanning Tree 最小生成树Shortest Path 最短路径Transitive Closure and Reduction 传递闭包Matching 匹配Eulerian Cycle / Chinese Postman Euler回路/中国邮路Edge and Vertex Connectivity 割边/割点Network Flow 网络流Drawing Graphs Nicely 图的描绘Drawing Trees 树的描绘Planarity Detection and Embedding 平面性检测和嵌入Graph Problems -- hard 图论-NP问题Clique 最大团Independent Set 独立集Vertex Cover 点覆盖Traveling Salesman Problem 旅行商问题Hamiltonian Cycle Hamilton回路Graph Partition 图的划分Edge Coloring 边染色Graph Isomorphism 同构Steiner Tree Steiner树Feedback Edge/Vertex Set 最大无环子图Computational Geometry 计算几何Convex Hull 凸包Triangulation 三角剖分Voronoi Diagrams Voronoi图Nearest Neighbor Search 最近点对查询Range Search 范围查询Point Location 位置查询Intersection Detection 碰撞测试Bin Packing 装箱问题Medial-Axis Transformation 中轴变换Polygon Partitioning 多边形分割Simplifying Polygons 多边形化简Shape Similarity 相似多边形Motion Planning 运动规划Maintaining Line Arrangements 平面分割Minkowski Sum Minkowski和Set and String Problems 集合与串的问题Set Cover 集合覆盖Set Packing 集合配置String Matching 模式匹配Approximate String Matching 模糊匹配Text Compression 压缩Cryptography 密码Finite State Machine Minimization 有穷自动机简化Longest Common Substring 最长公共子串Shortest Common Superstring 最短公共父串DP——Dynamic Programming——动态规划recursion ——递归第二部分、编程词汇A2A integration A2A整合abstract 抽象的abstract base class (ABC)抽象基类abstract class 抽象类abstraction 抽象、抽象物、抽象性access 存取、访问access level访问级别access function 访问函数account 账户action 动作activate 激活active 活动的actual parameter 实参adapter 适配器add-in 插件address 地址address space 地址空间address-of operator 取地址操作符ADL (argument-dependent lookup)ADO(ActiveX Data Object)ActiveX数据对象advancedaggregation 聚合、聚集algorithm 算法alias 别名align 排列、对齐allocate 分配、配置allocator分配器、配置器angle bracket 尖括号annotation 注解、评注API (Application Programming Interface) 应用(程序)编程接口app domain (application domain)应用域application 应用、应用程序application framework 应用程序框架appearance 外观append 附加architecture 架构、体系结构archive file 归档文件、存档文件argument引数(传给函式的值)。
About the T utorialD3 stands for Data-Driven Documents. D3.js is a JavaScript library for manipulating documents based on data. D3.js is a dynamic, interactive, online data visualizations framework used in a large number of websites. D3.js is written by Mike Bostock, created as a successor to an earlier visualization toolkit called Protovis. This tutorial will give you a complete knowledge on D3.jsframework.This is an introductory tutorial, which covers the basics of Data-Driven Documents and explains how to deal with its various components and sub-components.AudienceThis tutorial is prepared for professionals who are aspiring to make a career in online data visualization. This tutorial is intended to make you comfortable in getting started with the Data-Driven Documents and its various functions.PrerequisitesBefore proceeding with the various types of concepts given in this tutorial, it is being assumed that the readers are already aware about what a Framework is. In addition to this, it will be very helpful, if the readers have a sound knowledge on HTML, CSS and JavaScript.Copyright and DisclaimerCopyright 2017 by Tutorials Point (I) Pvt. Ltd.All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher.We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. If you discover any errors on our website or in this tutorial, please notify us at **************************T able of ContentsAbout the Tutorial (i)Audience (i)Prerequisites (i)Copyright and Disclaimer (i)Table of Contents (ii)1.D3.js – Introduction (1)What is D3.js? (1)Why Do We Need D3.js? (1)D3.js Features (1)D3.js Benefits (2)2.D3.js – Installation (3)D3.js Library (3)D3.js Editor (4)Web Browser (5)3.D3.js – Concepts (6)Web Standards (6)4.D3.js – Selections (10)The select() method (10)Adding DOM Elements (13)Modifying Elements (15)The selectAll() Method (19)5.D3.js – Data Join (20)What is a Data Join? (20)How Data Join Works? (20)Data Join Methods (23)Data Function (26)6.D3.js – Introduction to SVG (29)Features of SVG (29)A Minimal Example (29)SVG Using D3.js (31)Rectangle Element (33)Circle Element (35)Ellipse Element (36)7.D3.js – SVG Transformation (38)Introduction to SVG Transformation (38)A Minimal Example (40)Transform Library (46)8.D3.js – Transition (47)The transition() method (47)A Minimal Example (47)9.D3.js – Animation (49)The duration() Method (50)The delay() Method (51)Lifecycle of Transition (51)10.D3.js – Drawing Charts (53)Bar Chart (53)Circle Chart (57)Pie Chart (62)Donut Chart (69)11.D3.js – Graphs (73)SVG Coordinate Space (73)12.D3.js – Geographies (79)D3 Geo Path (79)Projections (80)13.D3.js – Array API (86)What is an Array? (86)Configuring API (86)Array Statistics API Methods (86)Array Search API Methods (90)Array Transformations API (92)14.D3.js – Collections API (95)Configuring API (95)Collections API Methods (95)15.D3.js – Selection API (103)Configuring the API (103)Selection API Methods (103)16.D3.js – Paths API (107)Configuring Paths (107)Paths API Methods (107)17.D3.js – Scales API (110)Configuring API (110)Scales API Methods (110)18.D3.js – Axis API (115)Configuring the Axis API (115)Axis API Methods (115)19.D3.js – Shapes API (119)Configuring API (119)Shapes Generators (119)Pies API (121)Lines API (122)20.D3.js – Colors API (124)Configuring API (124)Basic Operations (124)Color API Methods (125)21.D3.js – Transitions API (131)Configuring API (131)Transition API Methods (131)22.D3.js – Dragging API (134)Installation (134)Dragging API Methods (134)Dragging API - Drag Events (136)23.D3.js – Zooming API (137)Configuring API (137)Zooming API Methods (137)24.D3.js – Requests API (142)XMLHttpRequest (142)Configuring Requests (142)Requests API Methods (144)25.D3.js – Delimiter-Separated Values API (149)Configuring API (149)API methods (149)26.D3.js – Timer API (152)requestAnimationFrame (152)Configuring Timer (152)Timer API Methods (152)27.D3.js – Working Example (155)D3.js 5Data visualization is the presentation of data in a pictorial or graphical format. The primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics.Data visualization helps us to communicate our insights quickly and effectively. Any type of data, which is represented by a visualization allows users to compare the data, generate analytic reports, understand patterns and thus helps them to take the decision. Data visualizations can be interactive, so that users analyze specific data in the chart. Well, Data visualizations can be developed and integrated in regular websites and even mobile applications using different JavaScript frameworks.What is D3.js?D3.js is a JavaScript library used to create interactive visualizations in the browser. The D3.js library allows us to manipulate elements of a webpage in the context of a data set. These elements can be HTML , SVG , or Canvas elements and can be introduced, removed, or edited according to the contents of the data set. It is a library for manipulating the DOM objects. D3.js can be a valuable aid in data exploration, it gives you control over your data's representation and lets you add interactivity.Why Do We Need D3.js?D3.js is one of the premier framework when compare to other libraries. This is because it works on the web and its data visualizations are par excellence. Another reason it has worked so well is owing to its flexibility. Since it works seamlessly with the existing web technologies and can manipulate any part of the document object model, it is as flexible as the Client Side Web Technology Stack (HTML, CSS, and SVG). It has a great community support and is easier to learn.D3.js FeaturesD3.js is one of the best data visualization framework and it can be used to generate simple as well as complex visualizations along with user interaction and transition effects. Some of its salient features are listed below:∙Extremely flexible. ∙Easy to use and fast. ∙ Supports large datasets.1.D3.js∙Declarative programming.∙Code reusability.∙Has wide variety of curve generating functions.∙Associates data to an element or group of elements in the html page.D3.js BenefitsD3.js is an open source project and works without any plugin. It requires very less code and comes up with the following benefits:∙Great data visualization.∙It is modular. You can download a small piece of D3.js, which you want to use. No need to load the whole library every time.∙Easy to build a charting component.∙DOM manipulation.In the next chapter, we will understand how to install D3.js on our system.6D3.js 7In this chapter, we will learn how to set up the D3.js development environment. Before we start, we need the following components:∙D3.js library ∙Editor ∙Web browser ∙ Web serverLet us go through the steps one by one in detail.D3.js LibraryWe need to include the D3.js library into your HTML webpage in order to use D3.js to create data visualization. We can do it in the following two ways:∙Include the D3.js library from your project's folder. ∙ Include D3.js library from CDN (Content Delivery Network).Download D3.js LibraryD3.js is an open-source library and the source code of the library is freely available on the web at https:/// website. Visit the D3.js website and download the latest version of D3.js (d3.zip). As of now, the latest version is 4.6.0.After the download is complete, unzip the file and look for d3.min.js . This is the minified version of the D3.js source code. Copy the d3.min.js file and paste it into your project's root folder or any other folder, where you want to keep all the library files. Include the d3.min.js file in your HTML page as shown below.Example: Let us consider the following example.2.D3.js is a JavaScript code, so we should write all our D3 code within “script” tag. We may need to manipulate the existing DOM elements, so it is advisable to write the D3 code just before the end of the “body” tag.Include D3 Library from CDNWe can use the D3.js library by linking it directly into our HTML page from the Content Delivery Network (CDN). CDN is a network of servers where files are hosted and are delivered to a user based on their geographic location. If we use the CDN, we do not need to download the source code.Include the D3.js library using the CDN URL https:///d3.v4.min.js into our page as shown below.Example: Let us consider the following example.8D3.js EditorWe will need an editor to start writing your code. There are some great IDEs (Integrated Development Environment) with support for JavaScript like –∙Visual Studio Code∙WebStorm∙Eclipse∙Sublime TextThese IDEs provide intelligent code completion as well as support some of the modern JavaScript frameworks. If you do not have fancy IDE, you can always use a basic editor like Notepad, VI, etc.Web BrowserD3.js works on all the browsers except IE8 and lower.Web ServerMost browsers serve local HTML files directly from the local file system. However, there are certain restrictions when it comes to loading external data files. In the latter chapters of this tutorial, we will be loading data from external files like CSV and JSON. Therefore, it will be easier for us, if we set up the web server right from the beginning.You can use any web server, which you are comfortable with – e.g. IIS, Apache, etc.Viewing Your PageIn most cases, we can just open your HTML file in a web browser to view it. However, when loading external data sources, it is more reliable to run a local web server and view your page from the server (http://localhost:8080).93.D3.jsD3.js is an open source JavaScript library for –∙Data-driven manipulation of the Document Object Model (DOM).∙Working with data and shapes.∙Laying out visual elements for linear, hierarchical, network and geographic data.∙Enabling smooth transitions between user interface (UI) states.∙Enabling effective user interaction.Web StandardsBefore we can start using D3.js to create visualizations, we need to get familiar with web standards. The following web standards are heavily used in D3.js.∙HyperText Markup Language (HTML)∙Document Object Model (DOM)∙Cascading Style Sheets (CSS)∙Scalable Vector Graphics (SVG)∙JavaScriptLet us go through each of these web standards one by one in detail.HyperText Markup Language (HTML)As we know, HTML is used to structure the content of the webpage. It is stored in a text file with the extension “.html”.Example: A typical bare-bones HTML example looks like thisDocument Object Model (DOM)When a HTML page is loaded by a browser, it is converted to a hierarchical structure. Every tag in HTML is converted to an element / object in the DOM with a parent-child hierarchy. It makes our HTML more logically structured. Once the DOM is formed, it becomes easier to manipulate (add/modify/remove) the elements on the page.Let us understand the DOM using the following HTML document:The document object model of the above HTML document is as follows,11Cascading Style Sheets (CSS)While HTML gives a structure to the webpage, CSS styles makes the webpage more pleasant to look at. CSS is a Style Sheet Language used to describe the presentation of a document written in HTML or XML (including XML dialects like SVG or XHTML). CSS describes how elements should be rendered on a webpage.Scalable Vector Graphics (SVG)SVG is a way to render images on the webpage. SVG is not a direct image, but is just a way to create images using text. As its name suggests, it is a Scalable Vector. It scales itself according to the size of the browser, so resizing your browser will not distort the image. All browsers support SVG except IE 8 and below. Data visualizations are visual representations and it is convenient to use SVG to render visualizations using the D3.js.Think of SVG as a canvas on which we can paint different shapes. So to start with, let us create an SVG tag:The default measurement for SVG is pixels, so we do not need to specify if our unit is pixel. Now, if we want to draw a rectangle, we can draw it using the code below:13We can draw other shapes in SVG such as – Line, Circle, Ellipse, Text and Path.Just like styling HTML elements, styling SVG elements is simple. Let us set the background color of the rectangle to yellow. For that, we need to add an attribute “fill” and specify the value as yellow as shown below:JavaScriptJavaScript is a loosely typed client side scripting language that executes in the user's browser. JavaScript interacts with HTML elements (DOM elements) inorder to make the web user interface interactive. JavaScript implements the ECMAScript Standards , which includes core features based on ECMA-262 specifications as well as other features, which are not based on the ECMAScript standards. JavaScript knowledge is a prerequisite for D3.js.D3.jsSelections is one of the core concepts in D3.js. It is based on CSS selectors. It allows us toselect one or more elements in a webpage. In addition, it allows us to modify, append, or remove elements in a relation to the pre-defined dataset. In this chapter, we will see how to use selections to create data visualizations.D3.js helps to select elements from the HTML page using the following two methods:∙select() – Selects only one DOM element by matching the given CSS selector. If there are more than one elements for the given CSS selector, it selects the first one only. ∙selectAll() – Selects all DOM elements by matching the given CSS selector. If you are familiar with selecting elements with jQuery, D3.js selectors are almost the same.Let us go through each of the methods in detail.The select() methodThe select() method selects the HTML element based on CSS Selectors. In CSS Selectors, you can define and access HTML-elements in the following three ways:∙ Tag of a HTML element (e.g. div, h1, p, span, etc.,) ∙ Class name of a HTML element ∙ID of a HTML elementLet us see it in action with examples.Selection by TagYou can select HTML elements using its TAG. The following syntax is used to select the “div” tag elements, Example: Create a page “select_by_tag.html” and add the following changes, 4.By requesting the webpage through the browser, you will see the following output on the screen:Selection by Class nameHTML elements styled using CSS classes can be selected by using the following syntax.Create a webpage “select_by_class.html” and add the following changes:By requesting the webpage through the browser, you will see the following output on the screen.Selection by IDEvery element in a HTML page should have a unique ID. We can use this unique ID of an element to access it using the select() method as specified below.Create a webpage “select_by_id.html” and add the following changes.By requesting the webpage through the browser, you will see the following output on the screen.Adding DOM ElementsThe D3.js selection provides the append() and the text() methods to append new elements into the existing HTML documents. This section explains about adding DOM elements in detail. The append() MethodThe append() method appends a new element as the last child of the element in the current selection. This method can also modify the style of the elements, their attributes, properties, HTML and text content.Create a webpage “select_and_append.html” and add the following changes:17Requesting the webpage through browser, you could see the following output on the screen,Here, the append() method adds a new tag span inside the div tag as shown below:The text() MethodThe text() method is used to set the content of the selected / appended elements. Let us change the above example and add the text() method as shown below.1819Now refresh the webpage and you will see the following response.Here,the above script performs a chaining operation. D3.js smartly employs a technique called the chain syntax , which you may recognize from jQuery . By chaining methods together with periods, you can perform several actions in a single line of code. It is fast and easy. The same script can also access without chain syntax as shown below.Modifying ElementsD3.js provides various methods, html(), attr() and style() to modify the content and style of the selected elements. Let us see how to use modify methods in this chapter.The html() MethodThe html() method is used to set the html content of the selected / appended elements. Create a webpage “select_and_add_html.html” and add the following code.20By requesting the webpage through the browser, you will see the following output on the screen.The attr() MethodThe attr() method is used to add or update the attribute of the selected elements. Create a webpage “select_and_modify.html” and add the following code.21End of ebook previewIf you liked what you saw…Buy it from our store @ https://22。
2024年1月济南市高三期末学习质量检测英语试题本试卷共10页。
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第一部分阅读(共两节, 满分50分)第一节(共15小题;每小题2. 5分, 满分37. 5分)阅读下列短文, 从每题所给的A 、B 、C 和D 四个选项中选出最佳选项。
AA recent landing on the moon has awakened or renewed people’s enthusiasm for the stars and space exploration. Here are four trip ideas to inspire those would-be astronauts and astronomers.Kennedy Space Center AmericaThe NASA-operated Kennedy Space Center is a must for ambitious astronauts and space-lovers Hands-onexperiences range from live presentations delivered by astronauts to the new Astronaut Training Experience Center. Children aged 10 to 17 can experience spacewalking and exploring Mars.North York Moors, EnglandAs an International Dark Sky Reserve in the world, this lovely part of Yorkshire, England is host to the UK’s family-friendly National Parks Dark Skies festival. Well timed to the latter part of autumn half term in England, the festival includes bat-box making, evenings with winter birds and moonlit coastal walks.Pic du Midi, FranceThere are few observatories where you can observe stars before retiring to a comfortable cabin and watch the sunrise. Getting to the Pic du Midi Observatory is also an adventure by itself, involving a ride on two cable-cars up,.to a 2,877-meter-high mountain. The guided astronomy sessions help kids discover Saturn (土星) and its rings via powerful telescopes.Mount Teide, SpainHome to the largest solar observatory in the world, it sits on Spain’s highest mountain. Ride the cable-car up for a scientist-led tour, which includes the chance to observe the Sun through hand-held solar telescopes. The special family tour includes an attractive 90-minute workshop exploring how observatory physicists carry out their research.1. Which trip suits the teenagers expecting a face-to-face contact with astronauts?A. Kennedy Space Center.B. North York Moors.C. Mount Teide.D. Pic du Midi.2. What can visitors do on a trip to Yorkshire?A. Attend live presentations.B. Observe the rings of Saturn.C. Enjoy the sea view at night.D. Learn about physicists’ work.3. What do Pic du Midi and Mount Teide have in common?A. They accommodate family tourists.B. They include a tour led by scientists.C. They offer free hand-held telescopes.D. They are located on high mountains.BA rising star from Virginia has secured the title of “America’s Top Young Scientist” for his groundbreaking creation — a bar of soap designed to battle against skin cancer. At just 14 years old, Heman Bekele emerged as the victor of the 2023 Young Scientist Challenge, standing out among the ten finalists with his innovative creation known as the Skin Cancer Treating Soap (SCTS).Bekele’s brilliant concept centers on the development of a soap that is not only affordable, but also has the potential to reactivate the body’s natural defenders of the skin to stop skin cancer. In Bekele’s own words, “Curing cancer, one bar of soap at a time. ”He always has endless passion for biology and technology, and the Young Scientist Challenge just provided him with the perfect platform to display his ideas. Reflecting on his inspiration, Bekele shared that his childhood played a significant role in shaping his innovative thinking. Having witnessed people work tirelessly under the sun, he couldn’t help but wonder how many were aware of the risks associated with constant sun exposure.“I wanted to make my idea not only scientifically exceptional but also accessible to a broad audience,” Bekeleexpressed during an interview with the media. He received invaluable guidance from Deborah Isabelle, a product engineering specialist, who connected him with other scientists to aid him in reaching his ambitious plans.During his presentation, Bekele passionately expressed his vision of turning the soap into “a symbol of hope, accessibility, and a world where skin cancer treatment is within reach for all.”Over the coming five years, Bekele longs to perfect his invention and establish a nonprofit organization devoted to distributing his innovative creation to more places including undeveloped communities, offering hope and a practical solution in the fight against skin cancer.4. What made Bekele an instant hit?A. Starting a soap fashion.B. Overcoming skin cancer.C. Being the youngest scientist.D. Creating a soap against skin cancer.5. What inspired Bekele to invent SCTS?A. His concern for others.B. His adventure in childhood.C. His enthusiasm for technology.D. His interest in medical knowledge.6. What will Bekele do in the near future?A. Obtain official approval.B. Visit undeveloped areas.C. Increase the availability of the soap.D. Update the facilities of production.7. Which of the following can best describe Bekele?A. Inspiring and modest.B. Humorous and positive.C Creative and considerate. D. Curious and independent..CIn the animal world, speed is king. Fast animals have a leg up in outrunning other animals, which puts them high on the food chain. It would seem that all animals would go for speed, but then there’s the sloth (树懒). While a lion can go from 0 to 60 miles an hour in only five seconds, it takes a sloth all day to cover no more than 50 meters.Sloths live entirely in trees on a diet of leaves. And for this, they are extremely rare. While most of the land world is covered in trees, there are very few vertebrates (脊椎动物) that call the tree home. The aim of a 2016 study, says Jonathan Pauli, a University of Alabama professor of forest and wildlife ecology, was to help uncover why sloths are indeed so unique. “Among vertebrates, this is the rarest of lifestyles”, says Pauli. “When you picture animals that live off plant leaves, they are almost all big-things like deer. What’s super interesting about sloths is that they can’t be big.”For their research, Pauli and his Wisconsin team studied wild sloths at a field site. When the researchersmeasured the energy use of sloths, they found a wildly low burning of as little as 110 calories of energy a day. And for this, they take the cake: it is the lowest measured energetic output for any mammal (哺乳动物).“The measurement was intended to find out what it cost sloths to live over a day,”says Pauli, who adds that a diet of little but leaves lacks nutritional value and the animal’s small size doesn’t allow for overeating-so sloths need to find ways to make the most of their diets, which means using tiny amounts of energy, dramatic control of body temperature and living at an extremely slow pace.Their reward? A wonderfully widespread ecological system to call their own, one slow inch at a time.8. Why is a lion mentioned in Paragraph 1?A. To admire lions’ speed.B. To state sloths’ weakness.C. To confirm lions’ lead position.D. To highlight sloths’ uniqueness.9. What is the 2016 study mainly about?A. The lifestyle of sloths.B. The diet of vertebrates.C. The species of rare animals.D. The energy use of creatures.10. What does the underlined part “take the cake” in Paragraph 3 probably mean?A. Break down.B. Keep on.C. Stand out.D. Grow up.11. What can be inferred about sloths from Pauli’s words?A. Their slow pace is a balanced choice.B. They are in face of possible extinction.C. Their slow pace decides a tiny appetite.D. They suffer a lot against natural enemies.DFrom the day we’re born, curiosity becomes a primary driving force that motivates us to explore unknown ideas and territories in search of answers and stimulations. Human beings have an inborn desire to close the “curiosity gap” every day.A recent study found that curiosity can be a highly effective way to lead people to make smarter and healthier lifestyle choices. Evan Polman, assistant professor of marketing at the University of Wisconsin-Madison, said, “Our research shows that fueling people’s curiosity can influence their choices by turning them away from inviting desires, like unhealthy foods or taking the elevator, and toward less inviting but healthier options, such as buying more fresh produce or taking the stairs.”To prove the positive potential of the curiosity gap, Polman and his team conducted a series of experiments designed to test how curiosity affected the choices people made positively. In each study, arousing curiosityresulted in noticeable behavior change. For example, in one of the experiments, Polman increased the number of participants who chose to watch a video of academic nature by promising that they would reveal the secret behind a magic trick at the end of the video.The results of the field studies on curiosity were particularly convincing to Polman. In one field study, the researchers created a 10 percent increase in the use of stairs in a university building by posting trivial (琐事) questions near the elevators and posting the answers in the stairwell. In another, they increased the purchase of fresh produce by placing a joke on the posters describing the fruit or vegetable.Polman was surprised by the degree that taking advantage of the curiosity gap could motivate people to automatically make healthier lifestyle choices. He concluded, “Our results suggest that using interventions based on curiosity gaps has the potential to increase participation in desired behaviors for which people often lack motivation. It also provides new evidence that curiosity-based interventions come at an incredibly small cost and could help push people toward a variety of positive actions. ”12. What did a recent study find about curiosity?A. It fuels people’s desires.B. It lowers people’s buying.C. It benefits people’s health.D. It determines people’s lives.13. What are those experiments by Polman’s team aimed at?A. Supporting a finding.B. Raising a research topic.C. Arousing scientists’ interest.D. Displaying negative evidence.14. What is Polman’s attitude to the results of field studies?A. Doubtful.B. Unclear.C. Approving.D. Dismissive.15. What is a suitable title for the text?A. How to stay curiousB. The magic of curiosityC. How to make health choicesD. The two sides of curiosity gap第二节(共5小题;每小题2. 5分, 满分12. 5分)根据短文内容, 从短文后的选项中选出能填人空白处的最佳选项。
vc2010VC2010Introduction to Visual C++ 2010Microsoft Visual C++ 2010, also known as VC2010, is an Integrated Development Environment (IDE) that allows developers to create and manage C++ applications. It is part of the larger Visual Studio 2010 package, which includes tools for multiple programming languages. VC2010 is a popular choice among C++ developers due to its extensive features, powerful tools, and support for various platforms.Features and AdvantagesOne of the key features of VC2010 is its improved support for multi-core processing. It includes the Parallel Patterns Library (PPL), which enables developers to write parallel code more easily and efficiently. The Task Parallel Library (TPL) is also included, allowing developers to utilize task-based parallelism in their applications. This feature greatly enhances performance and scalability in multi-threaded applications.Another major advantage of VC2010 is the improved C++ language support. It introduces new features from the C++11 standard, including lambda expressions, auto keyword, and rvalue references. These features make the code more concise, readable, and easier to maintain. Additionally, the IDE includes a powerful IntelliSense feature that provides context-aware code completion, making development faster and more efficient.VC2010 also offers improved debugging capabilities. It includes a native debugger that allows developers to step through the code, set breakpoints, and inspect variables. The debugger integrates with other tools in the IDE, such as the Performance Profiler, which provides detailed performance analysis of the application. This makes it easier to identify and fix performance bottlenecks in the code.The IDE provides extensive support for application development on various platforms. Whether developing for Windows desktop, web, or mobile, VC2010 has the necessary tools and frameworks to build robust and efficient applications. It supports Windows Presentation Foundation (WPF) for building desktop applications with modern UI, for web development, and Windows Mobile for mobile application development.Integration with Other ToolsVC2010 seamlessly integrates with other tools and frameworks. It includes the Microsoft Foundation Classes (MFC) framework, which provides a set of reusable classes for creating Windows-based applications. The IDE also supports Active Template Library (ATL) for creating COM components and extensions.Additionally, VC2010 integrates with Team Foundation Server (TFS) for source control, versioning, and collaboration. This allows developers to work together on projects, track changes, and manage the development process effectively. The IDE also supports various testing frameworks, such as Microsoft Unit Testing Framework and Google Test, making it easy to write and execute tests for ensuring application quality.Migrating from Previous VersionsFor developers migrating from previous versions of VisualC++, VC2010 provides a smooth transition. The IDE supports project conversion, allowing developers to easily open and migrate projects from older versions. It also offerscompatibility with existing codebases, minimizing the effort required for migration.Support and CommunityVC2010 has a vast community of developers and enthusiasts who provide support and resources. Online forums, tutorials, and documentation are available to assist developers in learning the IDE and resolving issues. Microsoft also provides regular updates and bug fixes to ensure a stable and reliable development experience.ConclusionVC2010 offers a powerful and feature-rich development environment for C++ applications. Its extensive features, improved language support, and tight integration with other tools make it a popular choice among C++ developers. Whether developing for Windows desktop, web, or mobile, VC2010 provides the necessary tools and frameworks to create robust and efficient applications. With its supportive community and regular updates, VC2010 remains a reliable choice for C++ developers.。
00015⾃考英语(⼆)考纲精练试卷(⼆)及答案英语(⼆)⾃学教程标准预测试卷·考纲精练(⼆)第⼀部分:阅读判断A Dog's DilemmaFindi ng a ba by s itter w hile y ou go o ut t o w o rk i s,fo r example,an Incon v enI e n ce . Fo r the African w i ld dog,o ne of t h e c ont i nent's m o st e ndan g ere d c arni v ore s,it's a matter of lif e a nd de ath. N ew res earch sho ws th at o nce pa c ks fall below a c erta i n si z e,th e re are not enough animal s to bothhu nt food and st a y at hom e protecting the y oung.The Mrica n w ild dog h as dec.l ined drasticall y over the past c entury. Habitual loss,per s e c ut i on a nd une xp la i ne d o u tb re ak s o f di se ase h a v e all been blamed. Onl y 3,000. to 5,000 anim a l s re main,a nd th e s pe cies is expec t ed to g o ext in c t wi thin de c ad es if t he tr e n d c ontinu es.Oth er larg e c arniv o res such a s th e s pott e d h yena face s imilar p r ess ur e s,ye t a re n ot declin i ng. N o w Fr an c k Co urc ham p o f Ca m b ri dg e U ni v er s it y ha s fou nd a re a s o n why.The dog's w eakne ss lies i n it s s o c ial o r gani sa t i o n. W i t hin e a c h p ack of up to 20 adults and pups,o nly the dominant male and守f e mal e breed.Th ere mai n in g anim a l s help r ai se the pu ps,c oo pe ra t in g to hunt prey and defend the kill from o th e rc arm v o r e s.Becau se pup s can't ke e p up on a hunt,la r g e pa c k s le a ve an adult behind to protec t them from predators,which include lions and hyenas. But l ea v ing a b ab y sitter also carrie s cost s. A smaller hunting party i s less able to tackle la r ge prey a n d to defend the kill. There is also onele s s s toma c h in whi c h to ca rry food back to the den,and one more mouth to feed when the y get there.Courchamp in v e s ti ga t e d thi s awkward trade-off by modelling how the c o s ts of a ba bys itt erc h a n ge with decreasing pack s i ze. T hi s s howed that pac k s of more than fi ve adult s should be abl e to fe ed all t he pups and st ill spare a b a b ys i tt e r. Bu t wi th s m a ll e r pa c k s,e ith e r the huntin g or th e bab y sitting suffer s,or the animals have to compensate by in c r e a s in g t h e num ber of hunti ngexcursions ⼀whi c h it s elf carries a cost to the pack.Fi e ld ob s ervations in Zimbabw e supported the mode l. Pa cks o f fi ve ani m als or f ewer l eft p u ps unguarded more frequently than larger packs d i d. There w a s a l s o ev id ence th at w h e n t h ey qi d l eave a babysitter,they were forced to hunt m o re o ft e n..A pack which drops below a crit i cal s iz e be c om es c au g ht in a viciou s c ir cle,s a ys Cour c hamp,w h o is now at Paris-Sud University. "Po o r re pr od uc t i o n a nd low s u rv i va l f urth e r r e duc es pa c k s iz e,c u lminating in fai l ure of the whole pack. " A nd d ea th s ca u se d b y hum a n ac ti v it y,s a ys courchamp ',m ay be what reduces pac k n u mbers to be lo w th e s u s tainabl e thr es hold.Mammal ecol o gist C h ris Carbone at london’s I n s titute of Zoolo gy a gr e e s. Maintaining th e iintegrity r of w i ld dog packs will be vi t al in p r eserv in g th e s p ec i es,h e says.1.T h e African wild dog has been endanger e d.A. TrueB. FalseC. N ot Give n2.T h e spotted h y ena i s on the v erge of ext i nction.A. TrueB. FalseC. N ot Gi ve n3. Th e dog's weakness lies in its social o rg ani s a tion.A. TrueB. FalseC. N ot Gi ve n4. T h e remaining lions w ill d ie ou t wi th in de c ad es.A. TrueB. FalseC. Not Given5.Th e dominant female is always left be h i n d t o pro t ect the yo un g.A. TrueB. FalseC. N ot Gi ve n6. A smaller hunting party is also ab l e to t a c kl e large prey a nd do d e f e nd th e kill.A. TrueB. FalseC. N ot Gi ve n7. There is a tension between b ab ys i tti n g and hunt i n g.A. TrueB. FalseC. N ot Gi ve n8. The s ize of a pack must be big eno u g h for it to survive.A. TrueB. FalseC. N ot Gi ve n9. It is vital to maintain the integrity of wild dog packs.A. TrueB. FalseC. N ot Gi ve n10. Steps will be taken to protect the African wild dog.A. TrueB. FalseC. Not Given第⼆部分:阅读选择The Secret Cost of Using FacebookPeople are being lured(引诱) onto Facebook with the promise of a fun,free service,without realizing they're paying for it bygiving up loads of personal information. Facebook then attempts to make money by selling their data to advertisers that want to send targeted messages.Most Facebook users don't realize this is happening. Even if they know what the company is up to,they still have no idea what they're paying for Facebook,because people don't really know what their personal data is worth.The biggest problem,however,is that the company keeps changing the rules. Early on,you could keep everything private. That was the great thing about Facebook⼀-you could create your own little private network. Last year,the company changed its privacy rules so that many things-your city,your photo,your friends' names-were set,by default(默认),to be shared with everyone on the Internet.According to Facebook's vice-president Elliot Schrage,the company is simply making changes to Im proveIts servIce,and if people don't share information,they have a "less satisfying expenence."Some critics think this is more about Facebook looking to make more money. Its original business model,which involved selling ads and putting them at the side of the page,totally failed. Who wants to look at ads when they're online connecting with their friends?The privacy issue has already landed Facebook in hot water in Washington. In April,Senator Charles Schumer called on Facebook to change its privacy policy. He also urged the Federal Trade Commission to set guidelines for social-networking sites. "I think the senator rightly communicated that we had not been clear about what the new products were and how people could choose to use them or not to use them," Schrage admits.I suspect that whatever Facebook has done so far to invade our privacy,it's only the beginning.Which is whyI'm considering deactivating(撤销) my account. Facebook is a handy site,but I'm upset by the idea that my information is in the hands of people I don't trust. That's too high a price to pay.11. What do we learn about Facebook from the first paragraph?A. It is a website that sends messages to targeted users.B. It makes money by putting on advertisements.C. It pro v ide s load s of information t o it s u se r s.D. It profits by selling its users' personal data.12.What does the author say about most facebook users ?A. They care very little about ttheir personal information.B. They are reluctant to give up their personal information.C. They don't know their personal data enriches Facebook.D. They don't identify themselves when using the website.13. Why does Facebook make changes to its rules according to Elliot Schrage?A. To conform to the Federal guidelines.B. To improve its users' connectivity.C. To render better service to its users.D. To expand its scope of business.14. What does Senator Charles Schumer advocate?A. Setting guidelines for advertising on websites.B. Formulating regulations for social-networking sites.C. Banning the sharing of users' personal information.D. Removing ads from all social-networking sites.15. Why does the author plan to cancel his Facebook account?A. He finds many of its users untmstworthy.B. He is upset by its frequent rule changes.C. He doesn't want his personal data abused.D. He is dissatisfied with its current service.第三部分:概括段落⼤意和补全句⼦。
A dynamic replica management strategy in data grid--- Journal of Network and Computer Applications expired, propose, indicate, profitable, boost, claim, present, congestion, deficiency, moderately, metric, turnaround, assume,specify, display, illustrate, issue,outperform over .... about 37%, outperform ....lead todraw one's attentionaccordinglyhave great influence ontake into accountin terms ofplay major role inin comparison with, in comparison toi.e.=(拉丁)id estReplication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption.Managing this huge amount of data in a centralized way is ineffective due to extensive access latency and load on the central server.Data Grids aggregate a collection of distributed resources placed in different parts of the world to enable users to share data and resources.Data replication is an important technique to manage large data in a distributed manner.There are three key issues in all the data replication algorithms which are replica placement, replica management and replica selection.Meanwhile, even though the memory and storage size of new computers are ever increasing, they are still not keeping up with the request of storing large number of data.each node along its path to the requester.Enhanced Dynamic Hierarchical Replication and Weighted SchedulingStrategy in Data Grid--- Journal of Parallel and Distributed Computing duration, manually, appropriate, critical, therefore, hybrid, essential, respectively, candidate, typically, advantage, significantly, thereby, adopt, demonstrate, superiority, scenario, empirically, feasibility, duplicate, insufficient, interpret, beneficial, obviously, whilst, idle, considerably, notably, consequently, apparently,in a wise manneraccording tofrom a size point of viewdepend oncarry outis comprised ofalong withas well asto the best of our knowledgeBest replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time.Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner.Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available.Effective scheduling of jobs is necessary in such a system to use available resources such as computational, storage and network efficiently.Storing replicas close to the users or grid computation nodes improves response time, fault tolerance and decreases bandwidth consumption.The files of Grid environments that can be changed by Grid users might bring an important problem of maintaining data consistency among the various replicas distributed in different machines.So the sum of them along with the proper weight (w1,w2) for each factor yields the combined cost (CCi,j) of executing job i in site j.A classification of file placement and replication methods on grids--- Future Generation Computer Systems encounter, slightly, simplistic, clairvoyance, deploy, stringent, concerning, properly, appropriately, overhead, motivate, substantial, constantly, monitor, highlight, distinguish, omit, salient, entirely, criteria, conduct, preferably, alleviate, error-prone, conversely,for instanceaccount forhave serious impact ona.k.a.= also known asconsist inaim atin the hands offor .... purposesw.r.t.=with regard toconcentrate onfor the sake ofbe out of the scope of ...striping files in blocksProduction approaches are slightly different than works evaluated in simulation or in controlled conditions....File replication is a common solution to improve the reliability and performance of data transfers.Many file management strategies were proposed but none was adopted in large-scale production infrastructures.Clairvoyant models assume that resource characteristics of interest are entirely known to the file placement algorithm.Cooperation between data placement and job scheduling can improve the overall transfer time and have a significant impact on the application makespan as shown in.We conclude that replication policies should rely on a-priori information about file accesses, such as file type or workflow relation.Dynamic replica placement and selection strategies in data grids----Acomprehensive survey--- Journal of Parallel and Distributed Computing merit, demerit, tedious, namely, whereas, various, literature, facilitate, suitable, comparative, optimum, retrieve, rapid, evacuate, invoke, identical, prohibitive, drawback, periodically,with respect toin particularin generalas the name indicatesfar apartconsist of , consist inData replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth.Data replication enhances data availability and thereby increases the system reliability.Managing dynamic architecture of the grid, decision making of replica placement, storage space, cost of replication and selection are some of the issues that impact the performance of the grid.Benefits of data replication strategies include availability, reliability, scalability, adaptability and improved performance.As the name indicates, in dynamic grid, nodes can join and leave the grid anytime.Any replica placement and selection strategy tries to improve one or more of the following parameters: makespan, quality assurance, file missing rate, byte missing rate, communication cost, response time, bandwidth consumption, access latency, load balancing, maintenance cost, job execution time, fault tolerance and strategic replica placement.Identifying Dynamic Replication Strategies for a High-PerformanceData Grid--- Grid Computing 2001 identify, comparative, alternative, preliminary, envision, hierarchical, tier, above-mentioned, interpret, exhibit, defer, methodology, pending, scale, solely, churn outlarge amounts ofpose new problemsdenoted asadapt toconcentrate on doingconduct experimentssend it offin the order of petabytesas of nowDynamic replication can be used to reduce bandwidth consumption and access latency in high performance “data grids” where users require remote access to large files.A data grid connects a collection of geographically distributed computer and storage resources that may be located in different parts of a country or even in different countries, and enables users to share data and other resources.The main aims of using replication are to reduce access latency and bandwidth consumption. Replication can also help in load balancing and can improve reliability by creating multiple copies of the same data.Group-Based Management of Distributed File Caches--- Distributed Computing Systems, 2002 mechanism, exploit, inherent, detrimental, preempt, incur, mask, fetch, likelihood, overlapping, subtle,in spite ofcontend withfar enough in advancetake sth for granted(be) superior toDynamic file grouping is an effective mechanism for exploiting the predictability of file access patterns and improving the caching performance of distributed file systems.With our grouping mechanism we establish relationships by observing file access behavior, without relying on inference from file location or content.We group files to reduce access latency. By fetching groups of files, instead of individual files, we increase cache hit rates when groups contain files that are likely to be accessed together.Further experimentation against the same workloads demonstrated that recency was a better estimator of per-file succession likelihood than frequency counts.Job scheduling and data replication on data grids--- Future Generation Computer Systems throttle, hierarchical, authorized, indicate, dispatch, assign, exhaustive, revenue, aggregate, trade-off, mechanism, kaleidoscopic, approximately, plentiful, inexact, anticipated, mimic, depict, exhaust, demonstrate, superiority, namely, consume,to address this problemdata resides on the nodesa variety ofaim toin contrast tofor the sake ofby means ofplay an important role inhave no distinction betweenin terms ofon the contrarywith respect toand so forthby virtue ofreferring back toA cluster represents an organization unit which is a group of sites that are geographically close.Network bandwidth between sites within a cluster will be larger than across clusters.Scheduling jobs to suitable grid sites is necessary because data movement between different grid sites is time consuming.If a job is scheduled to a site where the required data are present, the job can process data in this site without any transmission delay for getting data from a remote site.RADPA: Reliability-aware Data Placement Algorithm for large-scale network storage systems--- High Performance Computing and Communications, 2009 ever-going, oblivious, exponentially, confront,as a consequencethat is to saysubject to the constraintit doesn't make sense to doMost of the replica data placement algorithms concern about the following two objectives, fairness and adaptability.In large-scale network storage systems, the reliabilities of devices are different relevant to device manufacturers and types.It can fairly distributed data among devices and reorganize near-minimum amount of data to preserve the balanced distribution with the changes of devices.Partitioning Functions for Stateful Data Parallelism in Stream Processing--- The VLDB Journal skewed, desirable, associated, exhibit, superior, accordingly, necessitate, prominent, tractable, exploit, effectively, efficiently, transparent, elastically, amenable, conflicting, concretely, exemplify, depict,a deluge ofin the form of continuous streamslarge volumes ofnecessitate doingas a examplefor instancein this scenarioAccordingly, there is an increasing need to gather and analyze data streams in near real-time to extract insights and detect emerging patterns and outliers.The increased affordability of distributed and parallel computing, thanks to advances in cloud computing and multi-core chip design, has made this problem tractable.However, in the presence of skew in the distribution of the partitioning key, the balance properties cannot be maintained by the consistent hash.MORM: A Multi-objective Optimized Replication Management strategyfor cloud storage cluster--- Journal of Systems Architecture issue, achieve, latency, entail, consumption, article, propose, candidate, conclusively, demonstrate, outperform, nowadays, huge, currently, crucial, significantly, adopt, observe, collectively, previously, holistic, thus, tradeoff, primary, therefore, aforementioned, capture, layout, remainder, formulate, present, enormous, drawback, infrastructure, chunk, nonetheless, moreover, duration, substantially, wherein, overall, collision, shortcoming, affect, further, address, motivate, explicitly, suppose, assume, entire, invariably, compromise, inherently, pursue, handle, denote, utilize, constraint, accordingly, infeasible, violate, respectively, guarantee, satisfaction, indicate, hence, worst-case, synthetic, assess, rarely, throughout, diversity, preference, illustrate, imply, additionally, is an important issuea series ofin terms ofin a distributed mannerin order toby defaultbe referred to astake a holistic view ofconflict witha variety ofis highly in demandgiven the aforementioned issue and trendtake into accountyield close toas followstake into considerationwith respect toa research hot spotcall foraccording todepend upon/onmeet ... requirementfocus onis sensitive tois composed ofconsist offrom the latency minimization perspectivea certain number ofis defined as (follows) / can be expressed as (follows) /can be calculated/computed by / is given by the followingat handcorresponding tohas nothing to do within addition toas depicted in Fig.1et al.The volume of data is measured in terabytes and some time in petabytes in many fields.Data replication allows speeding up data access, reducing access latency and increasing data availability.How many suitable replicas of each data should be created in the cloud to meet a reasonable system requirement is an important issue for further research.Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.As the system maintenance cost will significantly increase with the number of replicas increasing, keeping too many or fixed replicas are not a good choice.Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.We build up five objectives for optimization which provides us with the advantage that we can search for solutions that yield close to optimal values for these objectives.The shortcoming of them is that they only consider a restricted set of parameters affecting the replication decision. Further, they only focus on the improvement of the system performance and they do not address the energy efficiency issue in data centers.Data node load variance is the standard deviation of data node load of all data nodes in the cloud storage cluster which can be used to represent the degree of load balancing of the system.The advantage of using simulation is that we can easily vary parameters to understand their individual impact on system performance.Throughout the simulation, we assumed "write-once, read-many" data and did not include the consistency or write and update propagations costs in the study.Distributed replica placement algorithms for correlated data--- The Journal of Supercomputing yield, potential, congestion, prolonged, malicious, overhead, conventional, present, propose, numerous, tackle, pervasive, valid, utilize,develop a .... algorithmsuffer fromin a distributed mannerbe denoted as Mconverge toso on and so forthWith the advances in Internet technologies, applications are all moving toward serving widely distributed users.Replication techniques have been commonly used to minimize the communication latency by bringing the data close to the clients and improve data availability.Thus, data needs to be carefully placed to avoid unnecessary overhead.These correlations have significant impact on data access patterns.For structured data, data correlated due to the structural relations may be frequently accessed together.Assume that data objects can be clustered into different classes due to user accesses, and whenever a client issues an access request, it will only access data in a single class.One challenge for using centralized replica placement algorithms in a widely distributed system is that a server site has to know the (logical) network topology and the resident set of all structured data sets to make replication decisions.We assume that the data objects accessed by most of the transactions follow certain patterns, which will be stable for some time periods.Locality-aware allocation of multi-dimensional correlated files on thecloud platform--- Distributed and Parallel Databases enormous, retrieve, prevailing, commonly, correlated, booming, massive, exploit, crucial, fundamental, heuristic, deterministic, duplication, compromised, brute-force, sacrifice, sophisticated, investigate, abundant, notation, as a matter of factin various wayswith .... taken into considerationplay a vital role init turns out thatin terms ofvice versaa.k.a.= also known asThe effective management of enormous data volumes on the Cloud platform has attracted devoting research efforts.Currently, most prevailing Cloud file systems allocate data following the principles of fault tolerance and availability, while inter-file correlations, i.e. files correlated with each other, are often neglected.There is a trade-off between data locality and the scale of job parallelism.Although distributing data randomly is expected to achieve the best parallelism, however, such a method may lead to degraded user experiences for introducing extra costs on large volume of remote accesses, especially for many applications that are featured with data locality, e.g., context-aware search, subspace oriented aggregation queries, and etc.However, there must be several application-dependent hot subspaces, under which files are frequently being processed.The problem is how to find a compromised partition solution to well serve the file correlations of different feature subspaces as much as possible.If too many files are grouped together, the imbalance cost would raise and degrade the scale of job parallelism;if files are partitioned into too many small groups, data copying traffic across storage nodes would increase.Instead, our solution is to start from a sub-optimal solution and employ some heuristics to derive a near optimal partition with as less cost as possible.By allocating correlated files together, significant I/O savings can be achieved on reducing the huge cost of random data access over the entire distributed storage network.Big Data Analytics framework for Peer-to-Peer Botnet detection usingRandom Forests--- Information Sciences magnitude, accommodate, upsurge, issue, hence, propose, devise, thereby, has struggled toit was revealed thatis expanding exponentiallytake advantage ofin the pastin the realm ofover the last few yearsthere has also been research onin a scalable manneras per the current knowledge of the authorson the contraryin naturereport their work onNetwork traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time.In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system.As per the current knowledge of the authors, the area of network security analytics severely lacks prior research in addressing the issue of Big Data.Improving pattern recognition accuracy of partial discharges by newdata preprocessing methods--- Electric Power Systems Research stochastic, oscillation, literature, utilize, conventional, derive, distinctive, discriminative, artificial, significantly, considerably, furthermore, likewise, Additionally, reasonable, symbolize, eventually, scenario, consequently, appropriate, momentous, conduct, depict, waveshape, deficiency, nonetheless, derived, respectively, suffer from, notably,be taken into considerationby means ofto our best knowledgein accordance withwith respect toas mentionedwith regard tobe equal withlead tofor instancein additionin comparison toThus, analyzing the huge amount of data is not feasible unless data pre-processing is manipulated.As mentioned, PD is completely a random and nonlinear phenomenon. Since ANNs are the best classifiers to model such nonlinear systems, PD patterns can be recognized suitably by ANNs.In other words, when classifier is trained after initial sophistications based on the PRPD patterns extracted from some objects including artificial defects, it can be efficiently used in practical fields to identify the exactly same PD sources by new test data without any iterative process.In pulse shape characterization, some signal processing methods such as Wavelet or Fourier transforms are usually used to extract some features from PD waveshape. These methods are affected by noise and so it is necessary to incorporate some de-noising methods into the pattern recognition process.PD identification is usually performed using PRPD recognition which is not influenced by changing the experimental set up.Partial Discharge Pattern Recognition of Cast Resin CurrentTransformers Using Radial Basis Function Neural Network--- Journal of Electrical Engineering & Technology propose, novel, vital, demonstrate, conduct, significant,This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD).PD measurement and pattern recognition are important tools for improving the reliability of the high-voltage insulation system.。
How to Build Edge AI Applications that Work Examination of common challenges & issues encountered in real-world projectsChris Knorowski | August 2023Our TechnologyOur TechnologyA C O U S T I C E V E N TD E T E C T I O NA C T I V I T YR E C O G N I T I O NA N O M A L YD E T E C T I O NG E S T U R ER E C O G N I T I O N K E Y W O R DS P O T T I N GV I B R A T I O NC L A S S I F I C A T I O NSensiML Supported Development Kit Processor Wireless Gecko EFR32™ Arm® Cortex-M33, 32-bit (EFR32MG24)Silicon Labs xG24 Dev Kit (xG24-DK2601B)Pre-enabled SensorTypesTDK ICM-20648 6DoF accel + gyro (data collection firmware),TDK ICS-43434 microphone (left side mic is active in default data collection firmware) Additional AvailableSensorsSi7021 temp/humidity sensor, VEML6035light sensor, BMP384pressure sensor, CCS811 VoC sensor, Si7210 hall-effect sensor Available ExternalSensor InterfacesUART,I2C,SPI,ADC(12-bit@1Msps,***************)Pre-enabledConnectivityUSB, Serial, BLE 5.3 (integrated EFR32 multi-protocol wireless)ProgrammingEnvironmentIDEs: Silicon Labs Simplicity Studio IDECompilers: Simplicity Studio, gcc, IAR, KeilFirmware Flashing xG24 Dev Kit has built-in programming and debugger via microUSBconnection to PC, no separate board or debug cable req’dSensiML KnowledgePack FormatsBinary, Library, C SourceUseful Links SensiML Getting Started Guide, Solution Brief, Smart Building IoT VideoWorkshop, xG24 Smart Door Lock Demo4©2023 Silicon Laboratories Inc. All rights reserved.Understanding the Machine Learning WorkflowSteps to Build An Edge AI Model▪Knowledge PackData Capture LabAnalytics StudioKnowledge PackQualifying and ApplicationEdge AI: The Right Tool for the Application?S C A L A B L E S E C U R I T Y/P R I V A C Y L A T E N C YR E M O T E D E V I C E S/L I M I T E DP O W E R E C O N O M I C A LC O N N E C T I V I T YSteps to Build an Application that Uses Edge AIEdge AI Application Customer Engagement Process▪Customer wants feature X▪Do initial demo of tools/model with internal PoC similar to theiruse case convince yourselves this can be done and convincethem externally that the feature is possible.▪Come up with an SoW to work on a PoC for their specific application▪Come up with data collection protocol/plan for PoC▪Carry out data collection plan with customer device▪Build model▪Integrate model into PoC for test and validation▪Customer happy with PoC, but now wants it in the productWhat are the Keys to the Success of an Edge AI Application?M A C H I N E L E A R N I N GD ATA S C IE N C EA N A LY T I C S E MB E D D E D F I R M W A R EH A R D W A R EC O N N E C T I V I T YD O M A I N A N DB U S I N E S SE X P E R T I S EData Set CreationUnderstanding the Sensors for your ApplicationDefining the Model Scope and ContextRapid Model Test and Validation FrameworkTraining Neural Networks For Edge DevicesTraining Neural Networks for EdgeAI1.Collect your data set, then use augmentation to tailor it to the specificenvironment the model will operate in as well as expand the number of examples of each event2.Select a foundation model that was trained on a large corpus of keywords andacoustic sounds and has the appropriate architecturee transfer learning to train your new model to detect your specific keywordsalong with feature augmentation to prevent overfittinge quantization aware training to tune the model for an edge devicee post training quantization to quantize the model and make it suitable fordeploying at the edgeBuilding a Good Test Data SetDSP PreprocessingKnowledgePack – DSP Pipeline and Classifier Firmware•Segmentation can help reduce false positives and class confusion•Segmentation prevents running classifier unnecessarily, reducing power consumption •Feature extraction can make a complicated problem simple•Features with good class separation reduce the classifier complexity•Less features reduces the model complexity•Less model complexity -> lower latency and memory requiredEvent Triggering and SegmentationFeature Transformation/Extraction~150x3=450Use The Right Classifier For Your ApplicationAutoML Hyperparameter Search with Cross-Fold Validation▪Linear Regression models - << 1k▪KNN models 1 K < 50 K▪Decision Tree Models 10 K < 50 K▪Boosted Decision Tree Models 25 K < 200 K▪Bonsai Decision Tree Models 10K < 25 K ▪TensorFlow Models 25 K < 1 MBAutoML Rapidly searches across all classifiersQuestions?Visit us at https://Chris Knorowski | *************************** | August 2023。
A Model for Context-Aware Location IdentityPreservation using Differential PrivacyRoland AssamRWTH Aachen University,Germany assam@cs.rwth-aachen.deThomas SeidlRWTH Aachen University,Germany seidl@cs.rwth-aachen.deAbstract—Geospatial data emanating from GPS-enabled per-vasive devices reflects the mobility and interactions between people and places,and poses serious threats to privacy.Most of the existing location privacy works are based on the k-Anonymity privacy paradigm.In this paper,we employ a different and stronger privacy definition called Differential Pri-vacy.We propose a novel context-aware and non context-aware differential privacy technique.Our technique couples Kalman filter and exponential mechanism to ensure differential privacy for spatio-temporal data.We demonstrate that our approach protects outliers and provides stronger privacy than state-of-the-art works.Keywords-Differential Privacy,Data MiningI.I NTRODUCTIONThe widespread adoption of pervasive devices such as GPS-enabled(smart)phones,navigation systems and tablets does not only facilitate the connection of billions of people,but also leads to the generation of an avalanche of geospatial data daily in different cities around the world.Such data reflects the mobility and interactions between people and places,and poses serious threats to individuals’privacy,if it is tapped for trend analysis by profitable and non-profitable institutions. Gartner Research1strongly forecasts context-aware com-puting as the future of computing.Active research is be-ing pursuit in the area of context aware location mining. Sophisticated context aware data mining techniques without strong privacy guarantees will scare users from using location aware applications.The situation gets even grimmer,because some extremely good existing privacy solutions[1],[2]did not take into consideration the context of the location when anonymizing or obfuscation data.In fact,most of the existing location privacy techniques are based on the k-Anonymity[3] and l-Diversity[4]privacy definitions.The aforementioned privacy definitions require at least two mobile objects to achieve anonymity.In addition,they are still prone or exposed to background knowledge attacks,compositional[5]and other attacks.Location privacy can not afford to trail behind in context aware computing,which is seen as the worst threat to privacy.New location privacy research has the obligations to address and ensure privacy without losing the context of the original location.As a result of this,in this paper,we employ a privacy paradigm called differential privacy[6]and introduce a new privacy notion called context aware differential privacy. 1/technology/research/context-aware-computing/Providing privacy with a very strong privacy paradigm like differential privacy in context aware applications will have a profound impact er acceptance,subscription or usage of context aware systems in areas such as mobile social networking,health service patients surveillance,mobile telecommunications,national security,search engines,traffic monitoring,trend analysis and customer data mining.This paper has two goals.First,to protect the location identity of a single object or outliers objects.Secondly,to employ an alternative and far stronger privacy paradigm called differential privacy,which is resistant to background knowl-edge to protect multiple objects.In both cases,differential privacy is achieved in a context and non-context aware manner.A.Our ContributionsIt is very challenging to apply differential privacy in a geospatial context due to the difficulties that might arise during the derivation of the sensitivity in a geospatial metric space.[7],[8]and[9]also echoed the hurdles associated with the introduction of differential privacy in different metric spaces. In addition,naively perturbing spatio-temporal data without proper calibration of noise will yield meaningless results. In this paper,we address these difficulties and propose two differential private algorithms that ensure non-context aware and context aware location privacy respectively.Our differen-tially private location privacy techniques utilize the notion of exponential mechanism to guarantee privacy.Besides,many existing location or trajectory privacy works use k-Anonymity [1],[10],[11]or path obfuscation[12],[13],[14]to achieve privacy.We should note that in contrast to the above alluded existing works,we employ differential privacy instead of k-Anonymity to guarantee location privacy.Here is a summary of our contributions:•we rigorously derive the sensitivity of a geospatial context metric space using near optimal locations from a Kalman filter and randomly generated candidate obfuscated loca-tions by the server.•we propose a novel technique to achieve differentially private context aware and non-context aware location privacy.•Using real and synthetic datasets,we show that our technique outperforms state-of-the-art previous works.2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and CommunicationsFig.1:Differential private data interface for location privacy.B.System SetupSetup:Once an object emits a request to the Location Based Service(LBS)or Moving Object Database(MOD),itfirst passes through a trusted server.The trusted server employs differential privacy to obfuscate the location associated with the request before sending it to the LBS or MOD,as shown in Figure1as follows.The trusted server utilizes a Kalman filter to determine the near optimal location or true location of a point by eliminating GPS noise.Beside,it generates a series of candidate obfuscated locations.The true location and the candidate obfuscated locations are used by the exponential mechanism to create a differential private obfuscated location. Private Data Publication:This work employs non-interactive data publishing.In non-interactive data publishing, the data isfirst anonymized and then published,so that any data miner can have a copy of the published anonymized data. Paper Organization:The rest of this paper is organized as follows.Section I-C focuses on relevant related works.In Section II,some basic concepts of differential privacy and location obfuscation are explained.Section III provides the problem definitions and models Kalmanfilter for differential privacy.In section IV,we present our technique to achieve non-context aware and context aware differential privacy. Section V discuses the experimental results.In Section VI, afinal conclusion is made.C.Related WorksTrajectory Anonymization and Location Privacy:Some context-aware location privacy works include[15]and[16]. We ensure context-aware privacy using a trusted server while [16]provides privacy and anonymity directly in the LBS. Techniques such as[10],[17],[18]and[19]use the spatial k-Anonymity paradigm.The topography of these paradigms typically comprise of users who send their request through a trusted server to the LBS.Anonymization is accomplished in the trusted server.This is done by selecting an area called cloaking region(CR)and for a given object’s request,it ensures that at least k-1other object requests in that CR are sent to the LBS.Our approach is similar to these techniques only from the setup point of view;the trusted server of the former guarantees privacy through anonymization while our trusted server provides privacy through perturbation.While SpaceTwist[20]uses anchor location,our NNAR sharply differs from theirs.Unlike SpaceTwist that comprises of demand and supply spaces,our NNAR is basically used to add context to a differential private perturbed trace.[1] achieved k-Anonymity by generalization.[1]used inherent GPS error to propose a(k,δ)-Anonymity algorithm called Never Walk Alone(NWA)whereδrepresents the error radius. They achieved anonymity through space-translation in the process of co-location.While[1]is based on k-Anonymity ,our approach employs the differential privacy paradigm. Differential Privacy:Fundamental theories of differential privacy are provided in[6],and[21].We also employ some important guidelines and theories from[22],[9]to derive a sensitivity function for the trajectory metric space which is pivotal in the derivation of a differential private noise.The data access interface of PINQ[23]and[24]are used for interactive data publishing,while ours and[25]are geared to-wards non-interactive publishing.PINQ outputs private results for several data mining tasks while[24]is tailored for just the ID3algorithm.[26]utilized differential privacy to track commuters’pattern.[27]propose a differential private spatial decomposition technique which can be utilized to keep GPS traces private.It also provides a differential private version of quadtrees,kd-trees and Hilbert R-trees.Our technique differs from[27]in that we perturbed GPS traces within a Running Window while[27]presents a novel approach to minimize query error by configuring hierarchical noise parameters in a non-uniform manner.II.B ACKGROUNDA.Basics of Differential PrivacyDifferential privacy is a privacy paradigm proposed by Dwork[6]that ensures privacy through data perturbation.It is based on the core principle that for any two datasets that differ in only one entry,any randomized computation that delivers an almost identical result at the output is said to be differentially private.This is formally given as follows.Definition1:( -DIFFERENTIAL PRIVACY[22]):A ran-domization mechanism A(x)provides -differential privacy if for any two datasets D1and D2that differ on at most oneelement,and all output S⊆Range(A),P r[A(D1)∈S]≤exp( )∗P r[A(D2)∈S]The above definition simply means,the randomization process ensures that regardless if an individual chooses to include or remove her record from a dataset,there would be negligible change at the output,thus guaranteeing privacy. is the privacy parameter called privacy budget or privacy level. Sensitivity:In differential privacy,sensitivity is very critical during the process of noise derivation.Sensitivity is defined as the maximum change that occurs,if one record is removed from a dataset.Definition2:(L1SENSITIVITY[22]):The L1sensitiv-ity of a function f:D n→R d is the smallest number S(f) such that for all x and x which differ in a single entry, f(x)−f(x ) ≤S(f)Noise Addition:Differential privacy is achieved by adding noise to data.Three types of noise can be used.These include, the Laplace noise,the Gaussian noise and the Exponential Mechanism.This study uses the Exponential Mechanism. Exponential Mechanism:The exponential mechanism[21] extends the notion of differential privacy to incorporate non-real value functions.It guarantees differential privacy by approximating the true value of a data with the help of a quality function(which is also called the utility function). Specifically,it takes in several input data and maps them to some outputs.It then uses the quality function to assign scores to all the mappings.The output whose mapping has the best score is chosen and sampled with a given probability such that differential privacy is guaranteed.This is formally given as follows.Theorem1:[21]For a given input X and a function u: (X×y)→R,an algorithm that chooses an output y with aprobability∝exp(− u(X,y)2Δu)is -differential private. Composition:[23]mentioned that there are basically two types of compositions.These include,Sequential Composition and Parallel Composition.Sequential composition is exhibited when a sequence of computations provides differential privacy in isolation.Thefinal privacy guarantee is said to be the sum of each -differential privacy.On the other hand,parallel composition occurs when the input data is partitioned into disjoint sets,independent of the original data.In this case,the final privacy from such a sequence of computation depends on the worst computation guarantee of the sequence.B.Location ObfuscationLocation Obfuscation can be achieved by1)Hiding Loca-tions2)Inserting Dummy Regions3)Merging Regions4)Per-turbation.In this work,location obfuscation is accomplished by perturbation(using differential private noise).Location ob-fuscation techniques generally ensure privacy by degrading the true geographic location of an object.Most techniques[12], [13]usually define beforehand a region where the degraded location can lie on.This is then followed by the distortion of the true geographic location to any position inside the latter defined region.III.M ODELING L OCATION FOR P RIVACY Foundational theoretical works[6],[22]of differential pri-vacy explained how to achieve differential privacy for pred-icate outputs(i.e.0or1).However,many real life applica-tions have more complex outputs.Hence,in order to achievedifferential privacy for trajectories,trajectory data needs tobe intensively analyzed,re-defined and modeled to capturechanges in a trajectory metric space.A.Near Optimal Location EstimationKalman Filter:GPS measurements are associated witherrors.[28]and other GPS research works have shown thatKalmanfilter[29]is effective in enhancing the accuracy ofGPS measurements.A Kalmanfilter is a recursive algorithmthat is capable of determining the optimal estimate of cer-tain quantities when provided with a set of measurements.Specifically,a Kalmanfilter[30]iteration comprises of a TimeUpdate(prediction)and Measurement Update(correction)thatcompute the predicted state mean and covariances,as well asthe predicted measurement mean,covariance and the Kalmangain.The values determined from one iteration are given asfeedback to the next iteration until the posterior mean andcovariance of thefinal iteration is determined.Modeling Kalman Filter for Privacy:We use the Kalmanfilter to determine the optimal estimate of the true geographicallocation p t of a point at time t.As previously mentioned,GPSmeasurements are inaccurate.There are two kinds of errorsthat lead to this inaccuracy.These include,system error andmeasurement error.System error is the error associated withthe object’s state while measurement error originates from thesensor or measuring device.Consider that the true position(state)of an object at time t is denoted by p t.The systemerror is caused by an additive random noise v t.The stochasticdifference equation of the state is given byp t=F·p t−1+v t(1) where F is the state matrix and p t−1is the previous state.Onthe other hand,the GPS reading is observed with the help of ameasuring sensor.Let the sensor or measuring device’s mea-surement be denoted as q t and the noise associated with themeasuring device be denoted by w t.The stochastic differenceequation of the measuring device(observation)isq t=H·p t+w t(2) where H is the measurement matrix.In this paper,we assume that the object’s state noise v tand measurement noise w t are uncorrelated,zero mean whiteGaussian noise sequences,and at time t−1,the conditionalposterior probability of the object’s position P(p t−1|q t−1)is also Gaussian with meanμt−1and covarianceσt−1.Aftermodeling our Kalmanfilter as described above,we utilizethe Kalmanfilter algorithm tofind the optimal estimate ofthe true geographical location of a point as follows.TheKalmanfilter of our model requires at its input a sequenceof geographic positions in order to produce a near optimalestimate of a location p t at time t.Hence,we introduce atrusted server defined distance called Window Length(L),which refers to the Euclidean distance between an arbitrary location p t−n and the location we intend to estimate p t as shown in Figure1.When a request arrives at the trusted server, wefirst utilize L to determine the geographical position of the arbitrary point p k−n.Then all raw GPS points between p t−nand p t are grouped into a Window W.We apply the Kalmanfilter algorithm to the window,which recursively computes themeans and covariances during each iteration from time t−n tot.The estimate optimal location p t generated by the Kalman filter is a vital and pivotal parameter of the differential privaterandomization mechanism.Definition3:(R UNNING W INDOW):is a partitioned datablock that comprises of afinite amount of raw GPS spatio-temporal data.Creating High Precision Locations:A high precision geo-graphic location of an object is determined from a Windowby apply a Kalmanfilter using the raw GPS points within thatRunning Window.Definition4:(H IGH P RECISION T RACE):A High Preci-sion Trace is a spatio-temporal data whose geographicallocation is the optimal estimated location emanating from aKalman Filter within a Running Window.B.Problem DefinitionNotations:Let RTr i be a set of raw GPS points wherei∈{1,2,...n}.A single raw GPS point of RTr i is termeda Trace,and each trace given by(x i,y i,t i)correspondsto a geographic position(x i,y i)at time t i.The set of rawGPS traces RTr i does not represent the exact geographicpositions of an object because of the errors associated withGPS measurements.A more accurate geographic position ofan object called the high precision trace is determined bypartitioning RTr i into several Running Windows W j wherej∈{1,2,3,...m}m<n and running a Kalmanfilter algorithmin W j.Let HTr j denotes a high precision trace of W j.HTr jis considered as the location of the object.Like RTr i,the highprecision trace HTr j is a spatio-temporal data given by(x j,y j,t j).Definition5:(D IFFERENTIAL P RIVATE C ONTEXT A WAREL OCATION(DPCAL)):is an obfuscated location that fulfillsdifferential privacy and has a similar location context as thetrue location from which it was derived from.Definition6:(P ROBLEM D EFINITION1):Assume that anoutlier moving object M sends a sequence of raw spatio-temporal GPS data traces RTr i to a randomized mechanism A(x).Also consider that the mechanism periodically generatesa high precision trace HTr j of M using a Kalmanfilter inRunning Windows W j.Obfuscate the high precision traceHTr j by adding differential private noise to it(in both spaceand time domains)to produce an obfuscated trace HTr j,such that the -differential privacy condition is fulfilled.Theobfuscated trace HTr j should then be sent to the LBS orMOD.Definition7:(P ROBLEM D EFINITION2):Given the sameobject M and the assumptions used in Definition6,determinea Differential Private Context Aware obfuscatedtrace.2UWKRJRQDO3UR[LPLW\Fig.2:An OBRegion hosts several candidate traces.As a summary,this paper has two main goals.These include the use of differential privacy to ensure:1)Non-context aware(or Random)obfuscation and2)Context aware location obfuscation for outliers or multiple moving objects.C.Trajectory ObfuscationIn Section II-B,we mentioned that in existing location obfuscation techniques,the region on which the perturbed location can fall must be defined beforehand.In this work, such a region is termed the Obfuscation Region. Obfuscation Region:[13]uses circles to determine obfus-cation regions.Our obfuscation region(OBRegion)is a square grid with radius r o as depicted in Figure2.It is connected to an arm that spans from the latter grid to the moving object. Moreover,the perpendicular distance between an object and the obfuscation region is called the Orthogonal Proximity(ρ). Using such a structure ensures higher coverage for small grid radius.The trusted server is responsible for the determination of obfuscation regions and the obfuscation of traces as follows. Once a trace arrives at the trusted server,it uses some user specified distance parameters(r o andρ)to determine an obfuscation region.Then,it populates this region with a finite number of candidate obfuscation traces.Each of these candidate traces could be chosen to replace the true location (i.e.the High Precision Trace in Section III-A)of the object, thereby ensuring trace obfuscation.Candidate Trace Generation:There are two ways by which the server can generate candidate traces.1)By randomly picking afinite number of locations within the obfuscation region(during non-context aware location obfuscation).2)By choosing only locations within the obfuscation region that have the same location context as the true location of the object (during context aware location obfuscation).Formally:Definition8:(O BFUSCATION R EGION(OBR EGION)):is a square grid region that is determined by the trusted server with the use of a user defined radius.It is also home to the k candidate traces generated by the trusted server.The radius of the obfuscation region is denoted by r o.The radius of the OBRegion is defined by the user.If the user fails to enter this radius,the default radius of the trusted server is used.Besides,k is afinite positive integer.As expected,the server assigns only candidate locations.The time domain of each candidate location(except thefirst trace)is derived from the average velocity and distance of previous traces.IV.T RAJECTORY D IFFERENTIAL P RIVACYThe difficulty of practically implementing differential pri-vacy in other domains was alluded in Section I.This section provides a comprehensive research solution to this challenging problem for a trajectory metric space.A.Linking Differential Privacy to TrajectoryAs aforementioned in Section2,the exponential mechanism requires at its input among others1)input dataset2)output range and3)utility function.Input Dataset:The dataset of a Running Window is used as the exponential mechanism’s dataset.For example,assume that the dataset T1corresponds to a collection of raw GPS data within a given Running Window.Removing one raw GPS spatio-temporal data from that Running Window forms a new dataset T2such that T1and T2differ in just one single entry. T1and T2are sent as input dataset to a randomized mechanism A(x)as shown in Figure1.Output Range:Like other location obfuscation privacy techniques[12],[13],an obfuscation region that comprises of a set of locations is defined beforehand as described in Section III-C.In addition,we indicated that the trusted server determines an OBRegion and populates it with kfinite candidate obfuscation traces.We tightly and strictly followed the theoretical requirements[21]to create an output range. Also,we closely studied the nature of some output ranges utilized by some of the best practical existing differential private systems[21],[24];and similarly define an output range suited for our trajectory obfuscation metric space such that differential privacy is not violated.An obfuscated trace is destined to fall on an OBRegion.Since the theoretical concept dictates that an output range has to be made up of afinite set of elements,to define an output range for a trajectory metric space that comply with this rule,we needed to partition the OBRegion into sub regions.The subdivision of the OBRegion is performed by the central server as follows.The grid square of an OBRegion is(vertically)divided into N equidistance sub-regions.Each of the sub-region is called Sub-Obfuscation Region(Sub-OBRegion)and it is denoted by S i.Intuitively,after this division,the candidate traces are distributed into the N Sub-OBRegions as illustrated in Figure3.In this paper,the output range of the exponential mechanism is given by thefinite set of Sub-OBRegions that contain candidate traces.In order to prevent that no element in the output range should have a zero probability of being chosen,we have to ensure that no Sub-OBRegion is empty.Hence,Sub-OBRegions which do not contain candidate traces are discarded and the size of N is reduced.For example,the output range R for the OBRegion in Figure3is given by R={S1,S2,S3,S4,S5}.The k candidate traces are distributed within the different Sub-OBRegions.The Sub-OBRegion S3is discarded since no trace is found in it and N is updated to4.Quality or Utility Function:As mentioned before,we consider the high precision trace as the true location of an object.Intuitively,the closeness between a high precision traceK ZĞŐŝŽŶ^ƵďͲK ZĞŐŝŽŶƐ с ^ ͕͙ ^ ϭϱFig.3:Sub-OBRegion formed from an OBRegion.and a given Sub-OBRegion of the output range can be used to measure the quality of an obfuscated trace as depicted in Figure1.Hence,the utility function for our trajectory metric space is the Euclidean distance between the high precision trace and the center of a Sub-OBRegion as formalized in Equation3.U=−dist(HT r j−S c i)(3) where dist denotes the Euclidean distance,HT r j the high precision trace and S c i is the center of the i th Sub-OBRegion. Each Sub-OBRegion is given a score based on its distance from the high precision trace by using this utility function. The goal is tofind an Sub-OBRegion that is closest to the high precision trace.Hence,the smaller the distance,the higher the score.This accounts for the negative sign in the utility function in Equation3.Exponential Mechanism:The exponential mechanism will now map each Running Window(input dataset)to a given Sub-OBRegion(output range)and use the defined utility function to choose the optimum location which it can output as a good approximation of the original trace.We should note that the input dataset variables(raw GPS points from a GPS Device)are independent from the output range variables (traces generated by Server)since the former is retrieved from GPS readers while the latter is generated by the trusted server without any knowledge or consideration of the former.This independence is an important theoretical requirement when using exponential mechanism as stated by[21]and also re-iterated by Wasserman et al.[8].This further underlines that our exponential mechanism for a trajectory space meets all theoretical requirements.B.Sensitivity FunctionThe utility function U(HTr j,f(T1))reflects how good the output obfuscated trace HTr j∈S i is for a raw GPS dataset T1at a given running window.The sensitivity of the utility function measures the maximum possible change that will occur in a trajectory metric space when one raw GPS spatio-temporal point is removed from the dataset T1to form a dataset T2within a Running Window.This sensitivity is given by: S(f)=maxHTr j∈S i,T1,T2|UHTr j,f(T1)−UHTr j,f(T2)|(4) For each domain,the maximum change within a Running Window occurs if the trace with the smallest numerical value is removed.C.Differential Private Trajectory AlgorithmNoise Addition:Algorithm 1shows the differential pri-vate obfuscation algorithm,including the input parameters.Sequences of incoming raw GPS data are separated into Running Windows (Line 1)and are used to compute high precision traces in Line 2.Besides,the server determines the output range of the exponential mechanism by populating and computing the Sub-OBRegions with candidate traces in Line 3.Non-context aware or context aware candidate traces can be generated in Line 3depending on the candidate trace type C t .Non-context aware candidate traces are generated by default.The utility function in Line 4computes the score of each Sub-OBRegion,and all candidate traces within a given Sub-OBRegion are assigned the same score .The most profound step of our algorithm (Line 6)is the selection of a Sub-OBRegion based on the scores from the utility function.The exponential mechanism chooses the Sub-OBRegion using the best score with a probability proportional to exp2S (f ).U HTr j ,f (T ) .Thus,the likelihood for a Sub-OBRegion with a better score to be selected is of an exponential magnitude.Finally,a trace within the chosen Sub-OBRegion is sampled and sent to the MOD or LBS as a differential private obfuscated trace in Line 7.Analysis of Privacy Guarantee:All obfuscated traces emanating from the trusted server are differentially private.Theorem 2:Algorithm 1is -differentially private.Proof:In Line 6of algorithm 1,the probability of the exponential mechanism to choose a Sub-OBRegion is given by exp 2S (f ).U HTr j ,f (T 1) .|S i |i exp2S (f ).U HTr j ,f (T 2) d HTr j .|S i |where |S i |is the number of Sub-OBRegions.When the bestSub-OBRegion has been chosen,a trace within the selected Sub-OBRegion is uniformly sampled with a probability ∝exp 2S (f ).U HTr j ,f (T ).Since obfuscation occurs within a Running Window and we have a prior knowledge about the lower and upper bounds the sensitivity function,this means integrating exp2S (f ).U HTr j ,f (T ) delivers finite values.Hence sampling is being performed such that:P r A (T 1)=HTr j =exp2S (f ).U HTr j ,f (T 1)HTr j ∈S i exp2S (f ).U HTr j ,f (T 2) d HTr j Line 6is performed only once for a given Running Window.Hence according to theorem 1,Line 6guarantees 1×α-differential privacy.However,because a spatio-temporal data contains three dimensions,namely the X-position,Y-position and the time domain,the privacy budget needs to be carefully managed to control the cost of ing the Sequential Composition [23]described in Section II,the total cost of privacy in a Running Window to obfuscate the different dimensions is α.|D |.Where |D |is the number of dimensions and 2≤|D |≤3.The set of raw GPS traces from oneRunning Window do not intersect with raw GPS traces from another Running Window.Also,each of the candidate traces from one OBRegion do not reoccur in other OBRegions.Since each pairs of datasets (from a Running Window)and their corresponding output range (from candidate traces)are independent and disjoint from each other,it implies,following the principle of Parallel Composition [23],the privacy budget does not need to be shared across Running Windows.Hence each Running Window remains α.|D |-differential private.This means,if all domains of a trace are obfuscated (i.e.|D |=3)then each Running Window is 3α-differential private.On the other hand,if only the spatial domains of a trace are obfuscated,then each Running Window will be 2α-differential private.Thus,for a given Running Window dataset and its corresponding output range,each obfuscated trace sent to the MOD or LBS after selection by the exponential mechanism is α.|D |-differential private.Therefore,if an overall privacy budget is provided by thedata miner,for α=|D |,Algorithm 1is -differential private.Algorithm 1:Trace ObfuscationInput :Dataset T 1,privacy budget ,size of RunningWindow n ,OBRegion Radius r o ,N ,Orthogonal Proximity ρ,Candidate Trace Type C tOutput :differential private obfuscated Trace1Partition:Partition and group n raw GPS points into a Running Window2Kalman Filter:Compute a High Precision GPS trace from that Running Window using a Kalman filter 3Get Output Range:Use r o and ρto determine the OBRegion.Populate OBRegion w.r.t.C t and divide OBRegion into N Sub-OBRegions4Utility Function:Allocate scores to each Sub-OBRegion using the High Precision trace and the utility function in Equation 35Sensitivity:Get the sensitivity S (f )of the trajectory metric space using Equation 4,T 1and T 2;where T 2is formed by removing a point from T 1for each Running Window6Perturbation:Select an Sub-OBRegion and then choose a candidate trace within the latter region by sampling with noise whose probability is ∝exp2S (f ).U HTr j ,f (T )7return:the sampled trace and send to MOD or LBS D.Context Aware Location PrivacyContext Aware computing motivations were described in Section I.The second problem definition requires the guaran-tee of DPCAL.This is achieved using the Nearest Neighbor Anchor Resource (NNAR).NNAR:The main goal of the NNAR is to add more contextual meaning to noisy differential private traces.The server generates a resource pool based on the user’s current location.This is used in Algorithm 1to output a differential。