Introduction Information Filtering for Mobile Augmented Reality

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微小技术（Microsemi）产品用户指南：图像去噪滤波器50200643版本3.0说明书

UG0643User Guide Image De-Noising FilterMicrosemi HeadquartersOne Enterprise, Aliso Viejo,CA 92656 USAWithin the USA: +1 (800) 713-4113 Outside the USA: +1 (949) 380-6100 Sales: +1 (949) 380-6136Fax: +1 (949) 215-4996Email: *************************** ©2020 Microsemi, a wholly owned subsidiary of Microchip Technology Inc. All rights reserved. Microsemi and the Microsemi logo are registered trademarks of Microsemi Corporation. All other trademarks and service marks are the property of their respective owners. Microsemi makes no warranty, representation, or guarantee regarding the information contained herein or the suitability of its products and services for any particular purpose, nor does Microsemi assume any liability whatsoever arising out of the application or use of any product or circuit. The products sold hereunder and any other products sold by Microsemi have been subject to limited testing and should not be used in conjunction with mission-critical equipment or applications. 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Information provided in this document is proprietary to Microsemi, and Microsemi reserves the right to make any changes to the information in this document or to any products and services at any time without notice.About MicrosemiMicrosemi, a wholly owned subsidiary of Microchip T echnology Inc. (Nasdaq: MCHP), offers a comprehensive portfolio of semiconductor and system solutions for aerospace & defense, communications, data center and industrial markets. Products include high-performance and radiation-hardened analog mixed-signal integrated circuits, FPGAs, SoCs and ASICs; power management products; timing and synchronization devices and precise time solutions, setting the world's standard for time; voice processing devices; RF solutions; discrete components; enterprise storage and communication solutions, security technologies and scalable anti-tamper products; Ethernet solutions; Power-over-Ethernet ICs andmidspans; as well as custom design capabilities and services. Learn more at .Contents1Revision History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11.1Revision 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2Revision2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3Revision 1.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 3Image De-Noising Filter Hardware Implementation . . . . . . . . . . . . . . . . . . . . . . . . . .33.1Inputs and Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.2Configuration Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.3Testbench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.4Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.5Resource Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10FiguresFigure 1Median-Based Denoising Filter Effect on a Noisy Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Figure 2Image De-Noising Filter Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Figure 3Create SmartDesign Testbench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Figure 4Create New SmartDesign Testbench Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Figure 5Image De-Noise Filter Core in Libero SoC Catalog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Figure 6Image De-Noise Filter Core on SmartDesign Testbench Canvas . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Figure 7Promote to Top Level Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Figure 8Image De-Noise Filter Core Ports Promoted to Top Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Figure 9Generate Component Icon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Figure 10Import Files Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Figure 11Input Image File Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Figure 12Input Image File in Simulation Directory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Figure 13Open Interactively Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Figure 14ModelSim Tool with Image De-Noising Filter Testbench File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Figure 15Image De-Noising Filter Effect on a Noisy Image 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Figure 16Image De-Noising Filter Effect on a Noisy Image 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10TablesTable 1Image De-Noising Filter Ports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Table 2Design Configuration Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Table 3Testbench Configuration Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Table 4Resource Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Revision History1Revision HistoryThe revision history describes the changes that were implemented in the document. The changes arelisted by revision, starting with the most current publication.1.1Revision 3.0The following is a summary of the changes in revision 3.0 of this document.•Input Data_In_i is replaced with R_I, G_I and B_I to support RGB color format.•Output Data_Out_o is replaced with R_O, G_O and B_O.•Median Filter design logic is redesigned to support for (n x n) resolution with pipelined logics whereas previous design is implemented with Sequential FSM.1.2Revision2.0The following is a summary of the changes in revision 2.0 of this document.•In Image De-Noising Filter Hardware Implementation, page3:•YCbCr in signal names was replaced with Data.•The following text was deleted: The median filtering is only applied on the Y channel. The C B and C R signals are passed through the required pipe-lining registers to synchronize with Ychannel. For the Y channel, three pixels from each of the three video lines are read into threeshift-registers.•Details about the Image De-noising Filter testbench were added. For more information, see Testbench, page5.•The Timing Diagrams section and the appendix were deleted.•The number of buffers in the hardware was updated from four to five. For more information, see Image De-Noising Filter Hardware Implementation, page3.•Information about port widths was added. For more information, see Inputs and Outputs, page4.•Resource utilization data was updated. For more information, see Resource Utilization, page10.1.3Revision 1.0The first publication of this document.Introduction2IntroductionImages captured from image sensors are affected by noise. Impulse noise is the most common type ofnoise, also called salt-and-pepper noise. It is caused by malfunctioning pixels in camera sensors, faultymemory locations in the hardware, or errors in data transmission.Image denoising plays a vital role in digital image processing. Many schemes are available for removingnoise from images. A good denoising scheme retrieves a clearer image even if the image is highlyaffected by noise.Image denoising may either be linear or non-linear. A mean filter is an example of linear filtering, and amedian filter is an example of non-linear filtering. While the linear model has traditionally been preferredfor image denoising because of its speed, the limitation of this model is that it does not preserve theedges of the image. The non-linear model preserves the edges well compared to the linear model, but itis relatively slow.Despite the slowness, non-linear filtering is a good alternative to linear filtering because it effectivelysuppresses impulse noise while preserving the edge information. The median filter ensures that eachpixel in the image fits in with the pixels around it. It filters out samples that are not representative of theirsurroundings—the impulses. Therefore, it is very useful in filtering out missing or damaged pixels.For 2D images, standard median operation is implemented by sliding a window over the image. The 3 ×3 window size, considered to be effective for the most commonly used images, is implemented in the IP.At each position of the window, the nine pixel values inside the window are copied and sorted. The valueof the central pixel is replaced with the median value of the nine pixels in the window. The window slidesright by one column after every clock cycle until the end of the line. The following illustration shows theeffect of a median-based denoising filter on a noisy image.Figure 1 • Median-Based Denoising Filter Effect on a Noisy Image3Image De-Noising Filter HardwareImplementationMicrosemi Image De-noising Filter IP core—a part of Microsemi’s imaging and video solutions IP suite—supports 3 × 3 2D median filtering and effectively removes impulse noise from images.The Image De-noising Filter hardware contains three one-line buffers storing one horizontal video lineeach. The incoming data stream fills these three buffers, one by one. In the design illustrated in thisdocument, the median filter is implemented on 3 × 3 matrix, so three lines of video form the 3 × 3 windowfor the median. When the third buffer contains three pixel values, the read process is initiated.Three shift registers form the 3 × 3 2D array for median calculation. These shift registers are applied asinput to the median finder, which contains 8-bit comparators that sort the nine input values in increasingorder of magnitude and produce the median value, which is then updated into the output register. Thenew pixel column is shifted into the shift register, with the oldest data being shifted out. The 3 × 3 windowmoves from the left to right and from top to bottom for each frame.The following illustration shows the block diagram of the Image De-noising Filter hardware with defaultRGB888 input.Figure 2 • Image De-Noising Filter Hardware3.1Inputs and OutputsThe following table lists the input and output ports of the Image De-noising Filter.3.2Configuration ParametersThe following table lists the configuration parameters for the Image De-noising Filter design.Note:These are generic parameters that vary based on the application requirements.Table 1 • Image De-Noising Filter PortsPort Name Direction WidthDescriptionRESETN_I Input Active-low asynchronous reset signal to design SYS_CLK_I Input System clockR_I Input [(g_DATAWIDTH–1):0]Data input – Red Pixel G_I Input [(g_DATAWIDTH–1):0]Data input – Green Pixel B_IInput [(g_DATAWIDTH–1):0]Data input – Blue Pixel DATA_VALID_I Input Input data valid signal R_O Output [(g_DATAWIDTH-1):0]Data output - Red Pixel G_O Output [(g_DATAWIDTH-1):0]Data output - Green Pixel B_OOutput [(g_DATAWIDTH-1):0]Data output - Blue Pixel DATA_VALID_OOutputOutput data valid signalTable 2 • Design Configuration ParametersNameDescription Default G_DATA_WIDTH Data bit width 8G_RAM_SIZEBuffer size of RAM2048 (for horizontal resolution of 1920)3.3TestbenchT o demonstrate the functionality of the Image De-Noise Filter core, a sample testbench file (image-denoise_test ) is available in the Stimulus Hierarchy (View > Windows > Stimulus Hierarchy), and a sample testbench input image file (RGB_input.txt ) is available in the Libero ® SoC Files window (View > Windows > Files).The following table lists the testbench parameters that can be configured according to the application, if necessary.The following steps describe how to simulate the core using the testbench.1.In the Libero SoC Design Flow window, expand Create Design , and double-click Create SmartDesign Testbench, as shown in the following figure.Figure 3 •Create SmartDesign Testbench2.Enter a name for the SmartDesign testbench and click OK .Figure 4 •Create New SmartDesign Testbench Dialog BoxA SmartDesign testbench is created, and a canvas appears to the right of the Design Flow pane.3.In the Libero SoC Catalog (View > Windows > Catalog), expand Solutions-Video, and drag the Image De-Noise Filter IP core onto the SmartDesign testbench canvas.Table 3 • Testbench Configuration ParametersName Description CLKPERIOD Clock period HEIGHT Height of the image WIDTH Width of the image g_DATAWIDTH Data bit widthWAITNumber of clock cycles of delay between the transmission of one line of the input image and the next IMAGE_FILE_NAMEInput image nameFigure 5 • Image De-Noise Filter Core in Libero SoC CatalogThe core appears on the canvas, as shown in the following figure.Figure 6 • Image De-Noise Filter Core on SmartDesign Testbench Canvas4.Select all the ports of the core, right-click, and click Promote to Top Level, as shown in the followingfigure.Figure 7 • Promote to Top Level OptionThe ports are promoted to the top level, as shown in the following figure.Figure 8 • Image De-Noise Filter Core Ports Promoted to Top Level5.T o generate the Image De-noising Filter SmartDesign component, click Generate Component iconon the SmartDesign Toolbar, as shown in the following figure.Figure 9 • Generate Component IconA sample testbench input image file is created at:…\Project_name\component\Microsemi\SolutionCore\Image_Denoising_Fil-ter\1.2.0\Stimulus6.In the Libero SoC Files window, right-click the simulation directory, and click Import Files..., asshown in the following figure.Figure 10 • Import Files Option7.Do one of the following:•To import the sample testbench input image, browse to the sample testbench input image file, and click Open, as shown in the following figure.•To import a different image, browse to the desired image file, and click Open.Figure 11 • Input Image File SelectionThe input image file appears in the simulation directory, as shown in the following figure.Figure 12 • Input Image File in Simulation Directory8.In the Stimulus Hierarchy, expand Work, and right-click the Image De-noising Filter testbench file(image_denoise_test.v).9.Click Simulate Pre-Synth Design, and then click Open Interactively.Figure 13 • Open Interactively OptionThe ModelSim tool appears with the testbench file loaded on to it, as shown in the following figure. Figure 14 • ModelSim Tool with Image De-Noising Filter Testbench File10.If the simulation is interrupted because of the runtime limit in the DO file, use the run -all command tocomplete the simulation.After the simulation is completed, the testbench output image file (.txt) appears in the simulationfolder.3.4Simulation ResultsThe following illustration shows the effect of the Image De-noising Filter on a noisy image.Figure 15 • Image De-Noising Filter Effect on a Noisy Image 1Figure 16 • Image De-Noising Filter Effect on a Noisy Image 23.5Resource UtilizationIn this design, the Image De-noising Filter is implemented on an MPF300TS-1FCG1152I PolarFireSystem-on-Chip (SoC) FPGA. The following table provides resource utilization data for a 24-bit datawidth design after synthesis.Note:Image De-noising Filter supports for SmartFusion2 and PolarFire FPGAs.Table 4 • Resource UtilizationResource UtilizationDFFs19614_input LUTs2417MACC0RAM1Kx1815RAM64x180。

信息过滤(Information Filtering)综述

At a filtering server
– –
At the user site
– –
Filtering approach
Cognitive filtering
– –
Content-based filtering Document content vs user profiles Collaborative filtering, or properties-based filtering Similarity between users Recommendation systems User modeling & User clustering Complement for content-based systems
Implicit approach
– –
Explicit & Implicit approach
– –
三,IF系统的组成系统的组成
一般组成
(d) Learning Component updates feedback User personal details user profile relevant data items represented data items Information Provider
Statistical concept
User-model component:
–
Profile is a weighted-vector of index terms(such as: VSM, LSI) Correlation, Cosine measure Robertson&Sparck-Jones formula (PRM) (nave) Bayesian classifier Feedback, query reconstruction(such as: Rocchio)

大学英语四级阅读策略

Understanding the intention of questioning
Before starting to find the answer, carefully read the question, clarify the type of question, questioning method, and requirements.
Advisor the overall meaning of the passage and how specific details fit into that context
Understand context
Use words like "in other words," "that is," or "named" to help you understand the meaning of distinction or abstract concepts
Filtering irrelevant information: When searching for answers, pay attention to filtering irrelevant information and focus on relevant content.
Integrate information: Integrate the relevant information found to form a complete answer. If necessary, you can cite sentences or vocabulary from the original text to support your answer.

SAT写作指导英文SATWriting

Enhancing thinking ability
Writing helps to organize thoughts, analyze and solve problems, and improves logical thinking and judgment abilities.
Academic communication tools
Logical coherence
Reading and comprehension abilities
05
Reading skill
Identifying patterns in the text, such as organization, structure, and language use, aids in comprehension and evaluation of arguments
Variation in Sentence Length
Sentence structure and variation
Logical thinking ability
04
Clear viewpoints
Ensure that the article has a clear theme or argument and state it clearly at the beginning.
Sat Writing Guide English Satwriting
CATALOGUE
目录
Introduction Article structure and organization Language expression capability Logical thinking ability Reading and comprehension abilities Writing Strategies and Techniques Simulated test questions and analysis

English+Listening+Guidance+for+the+College+Entranc

Question types
Common question types include multiple choice, gap filling, note taking, and true/false or Yes/No questions
Problem solving techniques
To prepare for the listening test, students should practice active listening techniques such as predicting answers, summarizing information, and distinguishing between important and relevant information They should also familiarize themselves with the test format and question types to improve their family and confidence
Summary
Summary
This question is a true/false question It tests the student's ability to differentiate between facts and opinions in a given passage
Analysis
To answer this question correctly, the student needs to understand the overall structure of the passage and identify the missing information They should also pay attention to the context and use it to predict the possible answer

认知的九个层次英语作文

认知的九个层次英语作文The Nine Levels of Cognition。

Cognition is the process of acquiring knowledge and understanding through thought, experience, and the senses. It is a complex and multi-faceted concept that can be broken down into nine distinct levels. These levels represent the different ways in which we perceive, process, and interpret information, and they play a crucial role in shaping our thoughts, beliefs, and actions.The first level of cognition is sensation, which involves the immediate and direct experience of the world through the senses. This level is fundamental to all other levels of cognition, as it provides the raw data that our minds use to construct meaning and understanding.The second level of cognition is perception, which involves the organization and interpretation of sensory information. This level is where we begin to make sense ofthe world around us, and it is heavily influenced by our past experiences, beliefs, and cultural norms.The third level of cognition is attention, which involves the selective focus on certain aspects of our sensory experience. This level is crucial for filtering out irrelevant information and allowing us to concentrate on what is most important.The fourth level of cognition is memory, which involves the storage and retrieval of information. This level is essential for learning and decision-making, as it allows us to draw on past experiences and knowledge to inform our present actions.The fifth level of cognition is language, which involves the use of symbols and communication to convey meaning. This level is unique to humans and plays a central role in our ability to think, learn, and interact with others.The sixth level of cognition is reasoning, whichinvolves the use of logic and critical thinking to draw conclusions and solve problems. This level is essential for making sense of complex information and making informed decisions.The seventh level of cognition is learning, which involves the acquisition of new knowledge and skills. This level is a lifelong process that shapes our understanding of the world and our ability to adapt to new situations.The eighth level of cognition is creativity, which involves the generation of new ideas and solutions. This level is essential for innovation and problem-solving, and it allows us to think outside the box and explore new possibilities.The ninth level of cognition is metacognition, which involves the awareness and understanding of our own thought processes. This level is crucial for self-reflection and self-improvement, as it allows us to monitor and regulate our thinking and behavior.In conclusion, the nine levels of cognition represent the different ways in which we perceive, process, and interpret information. They play a crucial role in shaping our thoughts, beliefs, and actions, and understanding them can help us to better understand ourselves and the world around us. By recognizing the importance of each level and striving to develop our cognitive abilities, we can enhance our learning, reasoning, creativity, and self-awareness, and ultimately live more fulfilling and meaningful lives.。

大学英语听力2-Unit

information while listening.
Inference and inference
Inference and inference
This skill involves drawing conclusions from the information provided in a passage, often by connecting ideas or drawing parallels between different parts of the text. It is a critical thinking skill that helps listeners understand the deeper meaning of what is being said.
Movies and TV clips
• Movies and TV clips are longer listening materials that involve visual and auditory elements. They usually tell a story or present a specific idea through dialogue, sound effects, music, and visuals. Listeners need to pay attention to the dialogue, understand the storyline, analyze the characters, and interpret the visual and auditory elements to fully understand the content.
03
Listening strategies

Information Filtering

信息过滤提纲信息过滤概述(概念) 模式匹配(方法)垃圾邮件过滤(应用)信息过滤概述信息过滤概述——基本概念Google推出新闻过滤：https:///accounts/Login定义什么是信息过滤？是指计算机根据用户提供的一个过滤需求(userProfile)，从动态变化的信息流(比如Web, e-mail)中自动检索出满足用户个性化需求的信息。

Profile：一组对用户过滤需求的描述，这种“profile”描述了用户长期的、稳定的兴趣爱好近义术语信息的选择分发(Selective Dissemination ofInformation, SDI)，来自图书馆领域分流(Routing)，来自Message UnderstandingCurrent Awareness, 来自数据挖掘信息过滤的主要特点无结构的或半结构化的数据电子邮件是典型的半结构化数据结构化的邮件头无结构的邮件正文文本数据对用户profile的描述既可以用来屏蔽有害信息，也可以用来收集有益信息信息检索和信息过滤和其它概念的区别和文本分类(Categorization)的区别分类系统中的类不会经常改变。

相对而言，User Profile会动态变化和信息抽取(Information Extraction,IE) IF关心相关性IE只关心抽取的那些部分，不管相关性信息过滤的应用克服重复查询网络信息是动态变化的, 用户时常关心这种变化而在搜索引擎中, 用户只能不断地在网络上查询同样的内容, 以获得变化的信息, 这花费了用户大量的时间提供个性化信息服务对不同的用户采取不同的服务策略, 提供不同的服务内容。

实现“主动服务”，“信息找人”实现有害信息的过滤反动言论，保护国家安全谣言，保护社会稳定色情内容，保护青少年身心健康信息过滤的应用（续）垃圾信息过滤垃圾邮件垃圾短信推荐Recommendation根据不同用户之间需求的相关性推荐信息信息过滤概述——分类体系信息过滤系统分类示意图分类主动，还是被动主动过滤主动向用户推送相关信息被动过滤比如垃圾邮件过滤过滤操作的位置在信息源在过滤服务器上在客户端如：Outlook邮件过滤两种主要的过滤方法基于内容的信息过滤用户需求文档的形成及相关度的计算仅依靠信息的内容协作信息过滤合作式信息过滤被定义为“通过掌握一个用户群体的诸个体间的相互联系及组织关系来实现的信息过滤方法。

Integrate Knowledge

Antonio García Jiménez1, Alberto Díaz Esteban2, Pablo Gervás31Universidad Rey Juan Carlos (Madrid), 2CES Felipe II (Aranjuez), 3Universidad Complutense de MadridKnowledge Organization in a Multilingual System for the Personalization of Digital News Services: How to Integrate KnowledgeAbstract: In this paper we are concerned with the type of services that send periodic news selections to subscribers of a digital newspaper by means of electronic mail. The aims are to study the influence of categorisation in information retrieval and in digital newspapers, different models to solve problems of bilingualism in digital information services and to analyse the evaluation in information filtering and personalisation in information agents. Hermes∗ is a multilingual system for the personalisation of news services which allows integration and categorisation of information in two languages. In order to customise information for each user, Hermes provides the means for representing a user interests homogeneously across the operating languages of the system. A simple system is applied to train automatically a dynamic news item classifier for both languages, by taking the Yahoo set of categories as reference framework and using the web pages classified under them as training collection. Traditional evaluation methods have been applied and their shortcomings for the present endeavour have been noted.1. IntroductionThe recent boom in the popularity of the Internet has resulted in a rapid expansion of the range of information services available to the common user. One such service is that of systems offering to send users a selection of the daily news by e-mail. New ways of understanding information services and information systems are arising. In this paper we are concerned with the type of services that send periodic news selections to subscribers of a digital newspaper by means of electronic mail.The task of managing the volume of information that the advent of Internet has thrust into our hands faces two significant challenges. The first challenge is posed by the ever present globalisation, which demands a capability for dealing with information in several languages in a homogeneous manner. The second challenge is a much older problem but made severe by the sheer volume of material currently in circulation: how to classify documents with a minimum of effort in order to provide subsets of the whole to which a user interested in a particular topic can address himself without having to shift through the complete set. Once a system attempts to face both challenges at the same time, the problem grows. The main question to be faced is how to improve on a rough an ready initial classification of documents under language heading (documents in English and documents in Spanish) to achieve a classification by topic independently of the language employed. This may present additional problems of granularity of the classification, due to the fact that fine grained classifications in different languages soon lose any semblance of similarity that coarse grained classifications may have had. At a certain level Spanish categories for news items will branch off into a bullfighting section, whereas the English equivalent may branch off to cricket or baseball. This is∗ This work has been partially supported by Spanish Ministry of Science and Technology (Ref: 2000/020)not entirely a linguistic problem and is probably more related with cultural issues, but the problem remains and must be addressed.2. Resources for Multilingual Information AccessIt is very important for multilingual search to take into account both the growth of information services and monolingual digital libraries and the need for tools with multilingual capacity for information retrieval and extraction (Abaitua, 2000). An effective global information transfer faces up to the challenge posed by the large number of national languages in use. Language differences may become a barrier to information circulation in the world, among persons and among organizations. The access to foreign-language information can be facilitated by multilingual glossaries, thesauri and classifications (they can provide multilingual pointers to the subject matter of documents), and translations (Lancaster, 1992).The use of bilingual corpora is very interesting in the development of applications - as in terminology, automatic translation, and information multilingual search -, specially over the Internet. There are different kinds of multilingual corpora: corpora of texts in different languages to implement quantitative or statistical studies; comparable corpora, consisting of texts in a language and translations of similar documents in the same language; and parallel corpora, the same collection of texts in more than a language, - explicit correspondence relationships should be made between segments of each language, by means of grammatical categories.Asghar and Revie (2000) provide an interesting discussion of the role of thesauri and classifications in Internet: the growth of information in the worldwide Web and the migration of information resources to the new context demand a better and consistent subject identification; thesauri and classifications collaborate on description of information resources, avoiding problems associated with quality of information retrieved in the Web; thesauri and classifications improve the rapid and easy access to the information in the Web.Approaches to the construction of a new multilingual thesauri are: usual construction of a thesaurus, seeking equivalencies among terms collected (with different results among languages), without direct references to terms or structures of an existing thesaurus; translation of a monolingual thesaurus; conciliation and adaptation of existing thesauri in two or more languages. In truth, multilingual access to document collections is crucial. Besides, the co-operation improves the instruments connected with the information retrieval and the access to the information, in order to facilitate human and automatic indexation and to create links among related institutions (Lancaster, 1992; Clavel-Merrin, 1999).According to Aitchison and Gilchrist (1990), after verifying the suitability of the project, terms and categories of the thesaurus are translated with their equivalents. Documents in the source language are analyzed to assign them to categories (classification) or assigning different terms to each document in order to represent and to facilitate its retrieval (thesaurus). The last step is the formulation of the query in another language. By means of an automatic system, the user can search for terms with the equivalent terms in the original language as query.3. Multilingual Information Access in HermesHermes is a system that applies existing techniques from the field of text classification, text categorization (Sebastiani, 1999) and information retrieval (Salton,1989), besides user modelling (Amato & Straccia, 1999), to the selection of items, from different newspapers in different languages (Spanish and English), relevant for a user. Each user can create a profile in his language with his preferences and receive daily the news items that interest him from the different newspapers (Díaz et al., 2000).A user accesses the information server and registers for the service. The user selects his language and different data about his preferences (email address, days of the week to receive news, maximum number of items per message) and interests. These interests are: the sections of the newspapers, an alternative system of classification (first level of categories from Yahoo), and terms chosen by the user as interesting.The system manages two models per user, one per language, and applies each model to the news in the same language. The categories of Yahoo are language independent because there is a hierarchy in each language with the same first level categories. The terms are translated to from one language to the other.The message received by the user contains: the name of the user, the date, and a list of news items ranked according to the user information interests and respecting the maximum number of items per message defined. Each news item is presented with the source, the author, the title, a short summary adapted to the user (Acero et al., 2001), the relevance, and a link to the news item in the digital newspaper. At the end of the message appear the interests of the user as features in his profile in order to allow the user to check the true relevance of the received news.Finally, the system allows relevance feedback (Nakashima & Nakamura, 1997). The user can vote about the news in a positive, in a negative or in an indifferent way. This information is captured by the system in another interest for the user, the feedback terms that will be used in the next selection of news item.4. Multilingual Text Classification in HermesHermes uses three different systems for classifying information: one is the static classification of news items into sections provided by the newspaper domain, a second one is provided by a dynamic classification of the news items carried out automatically in terms of the categories used in the Yahoo directory, and a third one may be provided by the user as a custom-tailored category defined by a set of keywords and which is also automatically applied to the news items. The final classification is obtained by combining these sources through a weighted formula, according to a set of weights specified in the user model during configuration. These systems should ideally be as orthogonal as possible, in order to present truly different classifications of the domain. This is not the case altogether, but the overlap is not excessively significant.4.1 The Choice of CategoriesThe categories of Yahoo were chosen as a reference framework in the first approximation for various reasons generally related with the overall efficiency of the process. On one hand, they come associated with distinct sets of classified documents in different languages (those classified under the English and the Spanish versions of Yahoo). These sets of documents were easily accessible in electronic form and could be used to train the automatic classifier to be employed. On the other hand, they are a set of categories specifically designed to facilitate search through a heterogeneous collection of documents, such as is found in the web. It was hoped that the differences between the set of news items in your run-of-the-mill daily edition and the collection of documents available in the web would ensure that this second set of categories add information to the existing one in terms of newspaper sections.Various problems come associated with this choice. The automatic classifier is trained with documents corresponding to a domain other than the domain of application. The branching structure of the hypertext documents classified under each category implies that it is not always clear what page is an actual good example (possibly only leaves of the resulting hypertext trees should be used, cropping those intermediate pages which simply substructure a given category into subcategories but hold no relevant content themselves), and this introduces a degree of noise in the classification system. The effect of these problems in the evaluated results has been noted, and they are currently being explored in search of an optimized solution.4.2 Dealing with More than One LanguageIn Hermes each user builds a model defining his preferences over categories and keywords for a single language, and the system generates a model in the other language automatically. Information about newspaper sections is not generated in this way because it is language dependent. This is a clear instance of equivalence problems between languages, made even more acute by the fact that each newspaper may have its own set of sections, even if working in the same language. The technique employed for generating models in a different language is based on the translation of the keywords defined by the user. The use of Yahoo categories, together with the assumption that Yahoo categories across different languages match, simplifies the process. Once the models for the two languages have been built, the news items for each language are processed with respect to the corresponding version of the model. Each of the language specific classification processes is independent of the other.The final classification is carried out by combining the three different sources of classification through the weighted formula. Where automatic classification is required, it is achieved by calculating the one-to-one similarity between news items and the representation of the categories using the cosine formula of the Vector Space Model (Salton, 1989).The representation of each category is obtained by training with different documents associated to that category (Sebastiani, 1999). A possible solution to the problems outlined above concerning the disparity of domains resulting for this particular choice of set of categories would be to train the system with a manually classified set of real news items, but classified under the Yahoo system. This would represent an important volume of work and would lose the advantages of having a dynamically updated set of sample documents for the chosen categories, with matching representation in different languages. Alternative solutions would be to combine both types of documents in training, or to perform co-training (Blum & Mitchell, 1998) on the representation of the categories, using the daily set of correctly classified news items. Either solution would gather together the advantages of both approaches.5. Evaluation of Multilingual Information SystemsEvaluation of these new instruments requires: a reflection about categorisation, a validation of traditional evaluation measures within the new field of Internet, the consideration of the knowledge acquired during evaluation of search engines, and a close study of the working principles and the required evaluation according to the particular properties and conditions of the service under consideration.Although there are various procedures for the evaluation of information systems, the emergence of the particular combination of challenges, objectives and techniques involved in personalised news services gives rise to additional issues that need to beaddressed during system evaluation. On one hand, these systems have to ensure that the tools they provide for the user to specify his interest in information items of a particular type are sound according to traditional information retrieval measurements. On the other hand, they face a competitive market where different methods of specifying user interest are continuously competing for the user's eye, so any particular technique being employed must prove its worth in terms of user satisfaction. The following aspects must be covered in a thorough evaluation:a) categorisation, filtering, personalisation.b) user responsec) the vision that users develop of the systemd) user profilese) values of recall and precision for all the users on several specific daysIn order to achieve all these aims, explicit evaluations provided by the users are harvested for feedback on system response-time, ease of use, system efficiency, and conceptual and physical presentation. This information is compiled on the basis of a closed questionnaire with specific questions on the relevant main topics. The user is asked to evaluate aspects such as category overlap, category validity, relevance of a document for the assigned category, or quality of the overall category scheme.Additionally, a manual analysis of news items and user models logged by the system for a set of chosen days is carried out in terms of classic information retrieval measurements, which provide quantitative values for system efficiency .The experience of evaluating system performance and user satisfaction for different personalised news services (Díaz et al., 2000) has proven the importance of the nature of the information in this tasks, the relative merits of the three most popular methods of specifying information interests (sections, categories, and key words) with respect to this particular set of tasks, and the risks of careless application of recall and precision measures in systems such as these where different methods of specifying interests are combined (Díaz et al., 2001).An initial evaluation of a prototype of our system has given good feelings about the performance. This evaluation has been developed using a working pattern adapted to a monolingual version of the system used in previous experiments. This pattern includes several aspects as interface evaluation, newspaper sections, categories, summaries, bilingual capacity and user estimated recall and precision.In general, users found the system suitable. They are satisfied with the different aspects of the user model, they estimate that the translation of the keywords is sometimes less than adequate but they value in a positive way the possibility to receive news in different languages.We have yet to perform a more complete evaluation with a larger number of users and the relations between the different features that appear in our system must be studied in greater detail. For instance, how the multilinguality and the user modeling affect the traditional way of evaluating information retrieval systems, i.e. recall and precision measures.6. ConclusionsThis system can be a powerful tool in a multilingual context. In a globalized environment information services may take a principal role in overcoming linguistic and knowledge barriers, and contributing to the interrelation and even integration of cultures, economies and societies In truth, this integration depends on the efficiency of the system. The construction of this crucial instrument for the Information Societyrequires an evaluation that takes into account the user, the impact of automatic categorisation and user modelling, as well as the problems derived from the use of more than one language. Nonetheless, this tool will work in an integrating manner, from a cultural and knowledge perspective, whenever the contents that it helps to retrieve are specifically structured for this purpose - for instance, by respecting the differences between the different cultures, and supporting the common ground.ReferencesAbaitua, J. (2000). Tratamiento de corpora bilingües. In La ingeniería lingüística en la sociedad de la información. Held at Fundación Duques de Soria, Soria, 17-21 July2000. (Provisional version). [http://www.servinf.deusto.es/abaitua/ konzeptu/ta/soria00.htm]Acero, I., Alcojor, M., Díaz A. and Gómez J.M. (2001), Generación automática de resúmenes personalizados. Procesamiento del Lenguaje Natural,27, 281-188. Aitchison, J., Gilchrist, A. (1990). Thesaurus construction. A practical manual. 2º ed., London: Aslib.Amato, G. and Straccia, U. (1999). User Profile Modeling and Applications to Digital Libraries. In S. Abiteboul and A.M. Vercoustre (eds.), Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries,Lecture Notes in Computer Science, Springer-Verlag, vol. 1696, 184-197.Asghar Shiri, A., Revie, C. (2000). Thesauri on the Web: current developments and trends. Online Information Review, 24(4), 273-279.Blum, A., and Mitchell, T. (1998). Combining labelled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 92-100.Cacho, I., Latorre, A. (2001). Tesaurus multilingüe europeu sobre la sidas i la infecció pel VIH. In Cabré, M. T., Codina, Ll. I Estopá, R. (ed.), Terminología i Documentació. I Jornada de Terminología i Documentació, 24 May 2000.Barcelona: Institut Universitari de Lingüística Aplicada, U.P.F. p. 61-70.Clavel-Merrin, G. (1999). La necesidad de cooperación en la creación y mantenimiento de archivos temáticos multilingües de autoridades. In 65th IFLA Council and General Conference. Held at Bangkok, Thailand, 20-28 August, 1999.[/IV/ifla65/papers/080-155s.htm]Díaz, A., Gervás, P. and García A. (2000). Evaluating a User-Model Based Personalisation Architecture for Digital News Services. In Proceedings of the Fourth European Conference on Research and Advanced Technology for Digital Libraries, Lectures Notes in Computer Science, Springer Verlag, 259-268Díaz, A., Gervás, P., García, A., Chacón, I. (2001). Sections, categories and keywords as interest specification tools for personalised news services. Online Information Review, 25(3), 149-159.Lancaster, F. W. (1992). Vocabulary Control for Information Retrieval, 2ª ed.Arlington: Information Resources Press.Nakashima, T., and Nakamura, R. (1997). Information filtering for the Newspaper. In 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Held at Victoria, B.C., Canada, 20-22 August 1997.Salton, G. (1989). Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Reading, Massachusets: Addison-Wesley Sebastiani, F. (1999). A Tutorial on Automated Text Categorization. In Proceedings of the First Argentinean Symposium on Artificial Intelligence, 7-35.。

充满科技感的词汇

充满科技感的词汇尖端的技术cutting-edge (adj) technology信息爆炸Information explosion / overload信息时代the information age/ era网络的广泛使用the proliferation of the internet/ the extensive use of internet/ the widespread use of the internet科技创新/发展technological innovations / inventions/ advances/ progressions提高效率/ 生产力augment/ enhance/ boost efficiency/ productivity节省人力的机器labor-saving/ replacing machinery远程通讯telecommunication太空探索space exploration生物技术biotechnology省钱的cost-effective/ economical 自动化的automated脱离现实be detached from reality 不可想象的inconceivable可利用的available新颖的novel耐用的durable方便使用的user-friendly以惊人的速度at a staggering rate超轻的ultra-lightweight超薄的ultra-thin便携的portable垃圾邮件junk mail欺骗性的报道deceptive report少儿不宜inappropriate for minors 青少年犯罪juvenile delinquency黑客行为hacking信息过滤information filtering measurementn. 衡量、测量threemeasurements三围solarenergy太阳能solid a. 固体的、实心的、结实infinitepatience无限的耐心mechanicala. 机械的mechanicalengineering机械工程师mechanica.机械的precise a. 精确的precisionn. 精确logical a. 逻辑的logicn. 逻辑illogical a. 不合逻辑的psychological a. 心理的、心理学的psychologyn.心理phenomenonn. 现象、迹象phenomenala. 显著的1. 众所周知，在信息时代，互联网已经成为普通人最重要的信息来源之一。

商务英语谈判和会话unit1new

Leading questions
Questions that suggest an answer or bias the response in a specific direction
Perception skills
Logical arguments
Presenting facts, statistics, and evidence to support one's position
Negotiating differences
Resolving differences and negotiating solutions to problems
Phase of achieving an agreement
Summarizing the negotiation results
Summarizing the main points of the negotiation and highlighting the key issues
Start stage
Greeting and introduction
01
Expressing greetings and introducing one self
and the other party
Outlining the negotiation agenda
02
Briefly introducing the topics and objectives of
Open and Honey Communication
Use open and honey language to discover your ideas and positions, while examining the other party's views

informationretrievalIntroduction（PDF）

– R=f(Q,D,C)
信息检索就是给定一个查询Q，从文档集合C中计算每篇文档D与Q的相关度并排序(Ranking)
相关度通常只有相对意义，对一个Q，不同文档的相关度可以比较，而对于不同的Q的相关度不便比较
相关度的输入信息可以更多，比如用户的背景信息、用户的查询历史等
现代信息检索中相关度不是唯一度量，如还有：重要度、权威度、新颖度等度量。或者说这些因子都影响 “相关度”。
断也不尽相同
19
信息检索的基本概念
定义“相关性”的两个角度：
– 系统角度：系统输出结果，用户是信息的接受者。这种理解置用户于被动的地位，基于这种理解，研究的重心落在系统本身。主题相关性：检索系统检出的文档的主题即核心内容与用户的信息需求相匹配。系统角度相关并不和用户脱节。系统角度定义的相关简单可以计算。
4
2
Number of hosts (millions)
InItnertnerent:etP:aPsta,sPt,rePsrensnt,t,FuFututruere
140
120
100
The 'Network Effect’
80
kicks in, and the web
60
goes critical'
17
信息检索的基本概念
文档(Document)：检索的对象
– 可以是文本，也可以是图像、视频、语音等多媒体文档，text retrieval/image retrieval/video retrieval/speech retrieval/multimedia retrieval
– 可以是无格式、半格式、有格式的
12
Outline

CONTENT FILTERING FOR INFORMATION CENTRIC NETWORKS

摘要：A method provides for marking classifications of content objects at a content marking node in a content centric networking (CCN) network. The content marking node has a network interface with a network that is separately administered from the CCN network. The method includes receiving a content object on the network interface, checking whether the content object is authentic and there is a pending interest for the content object, and sending a request to a content marking service to obtain categories for the content object using a name of the content object. The method further includes receiving a response from the content marking service with the categories for the content object, and adding a category field to the content object, the category field including the categories.

listening performance,发生顺序 -回复

listening performance,發生順序-回复Listening Performance: The Sequence of EventsIntroduction:Listening is an essential skill in acquiring and comprehending information. It plays a crucial role in various aspects of our lives, including education, work, and social interactions. However, achieving effective listening performance is not an instantaneous process. It involves multiple stages that unfold gradually as individuals develop and refine their listening skills. In this article, we will delve into the sequence of events that contribute to one's listening performance.Stage 1: SensationThe first step in the sequence of listening performance is sensation. During this stage, an individual's ears pick up sound waves, which are then converted into electrical signals that travel to the brain through the auditory nerve. Sensation is a passive process that occurs automatically, without any conscious effort. It forms the foundation for subsequent stages of the listening process.Stage 2: PerceptionOnce the electrical signals reach the brain, they are processed and interpreted, leading to perception. Perception goes beyond mere hearing and involves making sense of the sounds and assigning meaning to them. During this stage, individuals analyze and interpret the auditory information, relying on their linguistic, cultural, and contextual knowledge to comprehend the message accurately.Stage 3: AttentionAttention is a critical component of effective listening performance. It refers to the ability to focus on relevant auditory information while filtering out distractions. Attention can be divided into two types: selective attention and divided attention. Selective attention involves focusing solely on a single source of sound, while divided attention involves the ability to process multiple sources of sound simultaneously.Stage 4: ComprehensionComprehension is the ultimate goal of listening performance. It encompasses understanding the intended message, extracting key information, and retaining it for future use. Successful comprehension relies on various factors, including vocabularyknowledge, grammar comprehension, and background knowledge about the topic being discussed or presented.Stage 5: RetentionRetention refers to the ability to remember and recall information from previous listening experiences. It is closely linked to comprehension and is influenced by factors such as the individual's memory capacity, encoding strategies used, and opportunities for reinforcement and repetition. Effective retention ensures that the acquired information stays in the long-term memory and can be retrieved when needed.Stage 6: ResponseThe response stage involves providing appropriate feedback or taking action based on the received information. It can take different forms, depending on the context. In an educational setting, response could involve answering questions, engaging in discussions, or completing assignments related to the listening material.Stage 7: Reflection and EvaluationOnce the listening task or activity is completed, individuals canreflect on their performance and evaluate their own listening skills. This stage allows for self-assessment and identification of areas for improvement. Reflecting on the listening experience helps individuals identify any difficulties encountered, strategies that were successful, and areas that need further development.Conclusion:Listening performance is a sequential process that involves multiple stages, starting from sensation and ending with reflection and evaluation. Each stage contributes to the overall effectiveness of listening skills. By understanding this sequence of events, individuals can actively work on improving their listening skills, leading to better comprehension, retention, and response. Developing effective listening skills is an ongoing process that requires practice, patience, and an understanding of the underlying components that contribute to successful listening performance.。

大学英语第三版精读

Listening and Speaking Training
Listening training
Provide diverse listening materials, such as news, movie clips, speeches, etc., to improve students' listening comprehension abilities.
要点二
Problem discussion
Propose questions about the theme, viewpoint, etc. of the article, guide students to think and discuss, and cultivate their critical thinking and expression abilities.
Learning progress
Observe the progress of students in the learning process, such as whether they can continuously improve their reading and writபைடு நூலகம்ng skills.
Check whether students have completed their assignments on time and the quality of their assignments.
Exam scores
Evaluate students' performance in various exams, such as mid-term and final exams.

英语四级仔细阅读介绍及技巧

Reading article types
Social issues
These are articles that discuss social issues such as power, race relationships, gender equality, and the environment They often provide arguments and perspectives on these issues
Detailed description
Read the question stem to determine the main idea of the article
Summary
Screening key information
Detailed description
When reading an article, pay attention to selecting key information related to the problem, such as important facts, data, viewpoints, etc., which are often crucial for solving the problem.
Headings and subheadings can help break down the text into smaller sections and provide an overview of the content They can also sign important information or changes in the topic
Exposure texts
These are texts that explain a concept or idea, such as scientific articles, technical manuals, and argumentative essays They are written in a formal language and have a logical structure with clear explanations

信息筛选作文800字

信息筛选作文800字英文回答：Information filtering is a technique used to reduce the amount of information that is presented to a user. This can be done by using a variety of methods, such as keyword filtering, content filtering, and collaborative filtering.Keyword filtering is a simple method of information filtering that uses a list of keywords to determine which information is presented to the user. When a user enters a query, the system searches for information that contains the specified keywords. Only the information that contains the keywords is presented to the user.Content filtering is a more sophisticated method of information filtering that uses a variety of techniques to determine which information is presented to the user. These techniques can include natural language processing, machine learning, and artificial intelligence. Content filteringsystems can be trained to identify specific types of information, such as news articles, blog posts, or videos. The system can then present the user with only the information that is relevant to their interests.Collaborative filtering is a type of informationfiltering that uses the preferences of other users to determine which information is presented to a user. Collaborative filtering systems collect data about the preferences of users, such as the items they have purchased, the movies they have watched, or the articles they have read. The system then uses this data to create a profilefor each user. When a user enters a query, the system compares their profile to the profiles of other users. The system then presents the user with information that is similar to the information that other users with similar profiles have enjoyed.Information filtering can be used to improve the user experience in a variety of ways. It can help users to:Find the information they are looking for more quicklyand easily.Avoid being overwhelmed by irrelevant information.Discover new and interesting information that they would not have otherwise found.中文回答：信息筛选是一种用来减少呈现给用户的信息量的技术。

信息屏蔽英文作文

信息屏蔽英文作文1. Information Overload。

With the advent of the digital age, we are constantly bombarded with information from various sources. Fromsocial media to email to news websites, we are constantly consuming information. However, this can often lead to information overload, where we are unable to process all the information that is being thrown at us. This can lead to stress and anxiety, as well as a decrease in productivity.2. The Need for Information Filtering。

To combat information overload, it is important tofilter the information that we consume. This can be done by setting priorities and focusing on the information that is most relevant to us. We can also use tools such as filters and alerts to ensure that we only receive the information that we need. By filtering out irrelevant information, wecan reduce the amount of time and energy that we spend on processing information.3. The Dangers of Information Filtering。

社交媒体对我们交流的影响英语作文

社交媒体对我们交流的影响英语作文英文回答：The advent of social media has revolutionized the way we communicate with each other. It has become an integral part of our lives, connecting us with friends, family, and colleagues across vast distances and breaking down geographical barriers. Social media has undeniably had a profound impact on our communication practices, shaping the way we express ourselves, consume information, and interact with others.Impact on Communication Styles and Language:Social media has introduced a new set of communication styles and linguistic conventions. The brevity and informality of platforms like Twitter and Instagram have influenced the way we write and speak, leading to the adoption of abbreviated language, emojis, and hashtags. This conversational style has seeped into our everydaycommunication, making it more casual and less formal.Information Consumption and Filter Bubbles:Social media has become a primary source of information for many people. However, it has also raised concerns about information filtering and the creation of "filter bubbles." Algorithms used by social media platforms tailor the content we see based on our preferences and past interactions. This can lead to a limited and biased view of the world, as we are less likely to encounter perspectives that challenge our own.Community Building and Relationships:Social media has enabled us to connect with like-minded individuals and form virtual communities. Online groups, forums, and discussion boards provide spaces for people to share their interests, exchange ideas, and build relationships. However, it is important to remember that online connections should not replace real-life interactions, and maintaining a balance between virtual andphysical communication is crucial for our well-being.Privacy and Personal Identity:The extensive use of social media can have implications for our privacy and personal identity. Sharing personal information and images online can leave us vulnerable to unauthorized access and misuse. It is essential to be mindful of our privacy settings and to consider thepotential risks before divulging sensitive information.Addiction and Mental Health:Social media addiction has emerged as a growing concern. Excessive use of these platforms can lead to negativemental health consequences, such as depression, anxiety,and sleep disturbances. It is important to set boundaries and take regular breaks from social media to maintain a healthy relationship with technology.中文回答：社交媒体对交流的影响。

2renti

2rentiIntroduction2renti is an online platform that connects rentiers (people who rent out their property) with tenants (people looking to rent a property). It provides a convenient and efficient way for both parties to find and communicate with each other. This document provides a comprehensive overview of 2renti, including its features, benefits, and how to use the platform.FeaturesProperty Listings2renti allows rentiers to easily create, manage, and display their property listings on the platform. Rentiers can provide detailed information about their properties, including location, size, amenities, and rental terms. The property listings are organized in a user-friendly format, making it easy for tenants to browse and search for the type of property they are looking for.Tenant ProfilesTenants can create profiles on 2renti, which include information about their preferences, budget, and desired rental location. This information helps rentiers in filtering potential tenants based on their preferences. Tenants can also provide feedback and ratings for the rentiers they have rented from in the past, helping other tenants make better-informed decisions.Messaging System2renti provides a messaging system that allows rentiers and tenants to communicate with each other directly. This feature enables faster and easier coordination between the two parties, facilitating the rental process. Messages can be sent and received within the platform, ensuring the privacy and security of both parties.Secure PaymentsTo ensure a secure and transparent payment process, 2renti integrates with popular payment gateways, allowing tenants to make payments directly through the platform. This eliminates the need for cash transactions and provides a record of all financial transactions for both rentiers and tenants.Reviews and RatingsBoth rentiers and tenants can leave reviews and ratings for each other after completing a rental transaction. This feedback is useful for future potential tenants and rentiers in making more informed decisions. It also encourages rentiers and tenants to maintain a high level of service and professionalism.BenefitsConvenience2renti simplifies the entire rental process by providing a centralized platform where rentiers and tenants can connect, communicate, and transact. It eliminates the need for multipleemails, phone calls, and physical visits, saving both parties time and effort.Wide Range of OptionsWith a large and diverse database of property listings, tenants have access to a wide range of options to choose from. Whether they are looking for a single room, an apartment, or a house, 2renti has something to suit every budget and preference.Trust and Security2renti verifies the identity of both rentiers and tenants, ensuring a higher level of trust and security. Verified profiles reduce the risk of scams, frauds, or dealing with unreliable individuals. The messaging system and secure payment integration also contribute to a more secure rental experience.Transparent and Fair Pricing2renti promotes transparency in pricing by allowing rentiers to set their rental rates based on market conditions. Tenants can compare prices and negotiate with rentiers if needed. This ensures fair pricing for both parties and helps create a competitive rental market.How to Use 2renti1.Create an Account - Visit the 2renti website andsign up as either a rentier or tenant. Provide the required information and complete the verification process.2.Create a Property Listing - For rentiers, create a property listing by providing details such as location, size, amenities, rental terms, and photos. Make sure to present your property in an appealing and accurate manner.3.Browse and Search Listings - As a tenant, explore the available property listings by using the search filters to find properties that match your preferences, budget, and desired location.4.Contact Rentier - Once you find a property that interests you, use the messaging system to directly contact the rentier. Ask any questions you have and request for more information or schedule a visit.5.View Property - Schedule a visit with the rentier to view the property in person. Take note of the condition of the property, amenities, and any other relevant details.6.Negotiate Rental Terms - If you are interested in renting the property, negotiate the rental terms with the rentier. Discuss the monthly rent, security deposit, duration of the lease, and any additional conditions or requests.7.Make a Payment - Once both parties agree on the rental terms, make the payment through the secure payment gateway integrated into the platform. Ensure that you understand the payment schedule and any additional fees.8.Review and Rate - After concluding the rental period, leave a review and rating for the rentier based onyour experience. Provide honest feedback to help futuretenants make better decisions.Conclusion2renti offers a convenient and efficient way for rentiers and tenants to connect and transact. With its features like property listings, tenant profiles, messaging system, secure payments, and reviews and ratings, 2renti provides a safe and reliable platform for renting properties. By using 2renti, both rentiers and tenants can save time, effort, and have a more streamlined rental experience.。

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Information Filtering for Mobile Augmented Reality∗Simon Julier,Marco Lanzagorta,Yohan Baillot and Dennis Brown†July2,2002IntroductionAugmented Reality(AR)has the potential to revolutionise the way in which information is delivered to a user.By tracking the user’s position and orientation,complicated spatial information can be directly registered to the real world in the context where it applies.We are focussing our research on the problem of developing mobile aug-mented reality systems which can be worn by an indi-vidual user operating in a large,complicated environment such as a city.Virtual sign posts can,for example,an-nounce the name of anonymous streets.Hidden infras-tructure such as sewer or gas lines can be shown beneath a road surface.However,an urban environment is ex-tremely complicated:it is populated by large numbers of buildings,each of which can have numerous facts stored about it.Therefore,it is very easy to inﬂict the user with information overload.This problem is illustrated in Fig-ure1which shows a screen capture from our mobile AR system1.The purpose of this application is simple:the system is trying to guide a user to an ofﬁce in a small building.The application should start by guiding the user to the correct building,then to the correct entrance,and ﬁnally to the correct ofﬁce.Figure1shows what happens when the system draws all the environmental data.The display includes both relevant information(such as the name and location of the building and the target ofﬁce) and irrelevant information(a detailed geometric model of ∗Portions of this paperﬁrst appeared in[1]†S.Julier,Y.Baillot and D.Brown are with ITT AES/Virtual Reality Laboratory,Naval Research Laboratory,Washington nzagorta is with Scientiﬁc and Engineering Solutions.1All the pictures for the AR system in this paper were captured by mixing the output of our AR system together with data from a video camera.The low quality of the images is due to limitations with the current camera and video mixer conﬁguration.If this paper is accepted, we shall obtain betterimages.Figure1:Showing all available data leads to clutter and confusion.the exterior of the building,the interior of the building, and all other data which lies within the view frustum but is behind the foreground building).As can be seen,the display is extremely complicated,confusing and uninfor-mative.To overcome these problems,we have begun to develop algorithms for informationﬁltering.These tools automat-ically restrict the information which is displayed to min-imise problems of information overload.Although the algorithms are being developed in the context of mobile augmented reality,they are drawn from several research areas and we believe that the basic approach is applicable in many other problem domains.Information Filtering Approaches Physically Based MethodsThe simplest way toﬁlter information is to use infor-mation about the physical infrastructure of the environ-1Figure2:Distance-based is not sufﬁciently discriminat-ing.Much irrelevant data is displayed.ment.In particular,it is possible to use distance-based and visiblity-basedﬁltering.Distance-basedﬁlters thresh-old an object’s visibility purely on the basis of its distance from the user.If the distance exceeds some threshold d, the object is not shown to the user.Many graphics APIs generalise this concept through the introduction of a level of detail:as the distance increases,progressively sim-pler models are used.Visibility-basedﬁlters determine whether an object is visible to the user and,if so,aug-ments the visible part.This has the advantage that much of the superﬂuous information behind the target building in Figure1is eliminated.However,such simple strategies are unsatisfactory be-cause importance is not simply a function of distance or visibilty from a user.The limitation of distance-basedﬁl-tering is shown in Figure2:the visibility distance d has been manually adjusted so that only the building which contains the ofﬁce is visible.However,to ensure that the target ofﬁce is visible,it is necessary to show a signiﬁ-cant amount of building infrastructure and other irrelevant information.Visibility-onlyﬁltering undermines the im-portant capability of providing a user with“X-ray vision”and be able to see information about objects which aren’t visible.Furthermore,it still does not identify important information.In Figure1all of the objects on the front of the building would still be annotated.Visibility FilteringSpatial Model of InteractionA more sophisticated version of distance-basedﬁltering is the spatial model of interaction[2].The spatial model wasﬁrst developed to consider the problems of awareness and interaction in multi-user virtual environments,where awareness can be used to determine whether or not an ob-ject is visible to,or capable of interaction with,another object.In this model,each object(e.g.,a user),is sur-rounded by a focus,speciﬁc to a medium(e.g.,graphics or sound),which deﬁnes the part of the environment of which the object is aware in that medium.Each object in the environment also has a medium-speciﬁc nimbus, which demarcates the space within which other objects can be aware of that object.If the focus and nimbus inter-sect,the two objects can interact with one another.The spatial model is a superset of simple visibility basedﬁltering.By allowing objects focuses and nimbuses to be expanded,it provides further distance related infor-mation.The spatial model has the advantage that it allows different objects to be demarcated at different ranges.Fur-thermore,it can leverage efﬁcient collision detection algo-rithms such as the Oriented Bounding Box Tree described in[3].Figure3(a)shows the results when the user is far away.The focus on the building and the entrance has been extended and therefore,they are the only objects which are visible.However,because the focus and nimbus are ﬁxed,as the user moves closer,the user automatically sees more(irrelevant)data,as shown in Figure3(b).Rule-Based FilteringSeveral researchers have addressed the problem ofﬁlter-ing through the use of inference engines and rule-bases. These are the most general form of informationﬁltering algorithm.Arbitrary relationships can be speciﬁed,main-tained and adjusted as a user’s context and goals change. KARMA[4],for example,used a rule-based approach to select relevant information to assist a user performing a maintenance and repair task.The user’s position and ori-entation,inter-object occlusion relationships,and the role that the objects play in a speciﬁc task to be accomplished by the user,all determine whether and how objects should be displayed,highlighted,and labeled on a tracked,see-2(a)At a distance,the spatial model can be used to discriminate be-tween only the most important information by expanding the nim-bus on far awayobjects.(b)However,as a user draws closer,their focus intersects with the nimbus of all objects,irrespective of their relevance.Figure 3:The Spatial Model of Interaction provides par-tial functionality required by an information ﬁltering sys-tem.Figure 4:Block diagram of the ﬁltering algorithm.through,head-worn display.However,the problem with this approach is its potential scalability concerns.The database of the examples shown in this paper includes 30buildings and over 740distinct objects,most of which are related to distant buildings which are simply not relevant to the current user’s task.Applying potentially computationally expensive,high or-der decision logic to even such a simple example has the potential to impose a substantial computational burden.When the system is to be applied to a large environment such as a city,the computational costs could become pro-hibitive.Hybrid Information Filtering SystemFrom the previous discussion,it is clear that the most gen-eral form of information ﬁltering is to use a rule-base.However,as explained above,it has signiﬁcant computa-tional concerns.The spatial model of interaction,to a ﬁrst order approximation,is capable of performing the initial ﬁltering which is required.Furthermore,it can leverage efﬁcient collision-detection algorithms.Therefore,our al-gorithm is a hybrid of these approaches,and consists of the four stages which are shown in Figure 4[1]:1.Initialize.Given knowledge of the user’s objectives and goals,calculate the user’s focus and the nimbus for each object.This calculation is carried out when-ever an object’s property changes or the user’s objec-tive e the spatial model of interaction to elimi-nate all objects whose nimbi do not intersect with the user’s focus.3.Reﬁne.Apply higher order decision logic.Stages 2and 3are performed periodically whenever the user’s position and/or orientation has changed.Our cur-rent implementation of Stage 2only uses the intersection 3of the focus and nimbus.However,other operations(such as visibility determination)could be incorporated as well. To implement this algorithm,it is necessary to repre-sent the user’s objectives and goals,the relevance of ob-jects to those goals,and provide a mechanism for calcu-lating the focus and nimbus.We encode the notion of objectives and goals through the use of objective and sub-jective states which are assigned to each object and each user.Objective properties are the same for all users,irrespec-tive of the tasks which that user is carrying out.Such properties include the object’s classiﬁcation(for example whether it is a building or an underground pipe),its loca-tion,its size and its shape.This can be extended by noting that many types of objects have an impact zone—an ex-tended region over which an object has a direct physical impact.A wireless networking system such as the Wave-LAN,for example,is effective over aﬁnite distance.This region can be represented as a sphere whose radius equals the maximum reliable transmission range.Conversely,a more accurate representation could take account of the masking and multi-path effects of buildings and terrain through modeling the impact zone as a series of intercon-nected volumes.Because of their differing physical prop-erties,different media can have different impact zones. Subjective properties attempt to encapsulate the domain-speciﬁc knowledge of how a particular object re-lates to a particular task for a particular user.Therefore, they vary between users and depend on the user’s task and context.We represent this data using an importance vector.The importance vector stores the relevance of an object with respect to a set of domain-speciﬁc and user-scenario speciﬁc criteria.For example,if a user is follow-ing a route to a particular ofﬁce,only that ofﬁce and route information which leads to it is important—all other in-formation is less important.The objective–subjective property framework can be applied to model the state of each user.Each user has their own objective properties(such as position and ori-entation)and subjective properties(which refer directly to the user’s current tasks).Analogous to the importance vector we deﬁne the task vector which stores the rele-vance of a task to the user’s current activities.The use of a vector means that a user can carry out multiple tasks simultaneously and,by assigning weights to those tasks, different priorities can be assigned.For example,at a cer-tain time a user might be given a task to follow a route between two points.However,the user is also concerned that(s)he does not enter an unsafe environment.There-fore,two tasks—route following and avoiding unsafe ar-eas—run concurrently.The task vector is supplemented by additional ancillary information.In the route follow-ing task,the system needs to store the way points and the ﬁnal destination of the route.ExampleThe scenario is that a mobile user will be given directions to the location of Simon’s Ofﬁce.The system is illustrated in Figure5,which shows the output of the system in three separate locations2.Figure5(a),taken from the same position as that used in Figure3(b)shows that the second stage of theﬁlter eliminates all superﬂuous data not relevant to the route following task.Therefore,only the entrance to the build-ing is displayed.Figure5(b)is taken inside the building.A route has appeared,directing the user towards the of-ﬁce.Due to the action of the spatial model,only a subset of the route is shown at any given time to avoid confus-ing the user.In Figure5(c),the user draws close to the ﬁnal destination.The display shows aﬁnal turn to the left (potentially confusing in Figure5(b))and theﬁnal desti-nation ofﬁce.Figure5(b)shows a limitation with our current imple-mentation.The blue rectangle to the left of the image is actually the front of the target building.This is a route-related object whose nimbus extends inside the building and therefore theﬁlter determines it is relevant to the user. There are a number of ways to eliminate this artifact in-cluding the use of visibility information(in stage3of the ﬁlter),or redeﬁning the task with aﬁner granularity.For example,the task could be decomposed into two tasks of entering the correct building and traversing to the correct ofﬁce within that building.2It should be noted that,to date,tracking systems which operate in-doors,outdoors and could be deployed over the area of a building are still not available.For the purpose of this article,we assume that such tracking systems exist.For a review of current work in tracking systems, see the upcoming IEEE Computer Graphics and Applications special is-sue on tracking.4(a)View from the door,same as in Fig-ure 3(b).Only the building and the correct entrance areannotated.(b)View along corridor inside building.A route leads towards the ﬁnaldestination.(c)As the user draws near the ﬁnal destina-tion,the destination ofﬁce is shown as well as a ﬁnal turn in the route.Figure 5:Sequence from example.See text for a descrip-tion.ConclusionsIn this paper we have discussed information ﬁltering al-gorithms particularly tailored for the needs of mobile aug-mented reality systems.We have presented a hybrid sys-tem which allows the use of arbitrarily complicated de-cision models but,at the same time,can leverage spatial operators to signiﬁcantly reduce scaling.However,the work described in this paper only ad-dresses the ﬁrst of several stages required to build in-formative user interfaces.First,it is necessary to man-intain visual constraints between the objects to be anno-tated and the annotations themselves.Blaine et al.refer to the maintenance of these constraints as view management and demonstrate algorithms which automatically size and position virtual labels such that the labels do not overlap one another or the objects which they are augmenting [5].Second,it is unlikely that pixel-level registration can be achieved with wearable tracking systems.MacIntyre et al.have begun to develop algorithms to quantify regis-tration errors to dynamically adjust augmentation to min-imize potential ambiguities [6].Both of these extensions introduce a coupling between objects which are ﬁltered out and those which are not.Our current work is extend-ing the ﬁltering algorithm to explore these interdependen-cies.References[1]S.Julier,nzagorta,S.Sestito,L.Rosenblum,T.H¨o llerer and S.Feiner,“Information Filtering for Mobile Augmented Reality,”in Proceedings of the IEEE 2000International Symposium on Augmented Reality,Germany ,IEEE,October 2000.[2]S.Benford and L.Fahl´e n,“A Spatial Model of In-teraction in Large Virtual Environments,”in Proceed-ings of ECSCW ’93,(Milan,Italy),September 1993.[3]S.Gottschalk,M.C.Lin and D.Manocha,“OBB-Tree:A Hierarchical Structure for Rapid InterferenceDetection,”Computer Graphics ,vol.30,no.Annual Conference Series,pp.171–180,1996.[4]S.Feiner, B.MacIntyre and D.Seligmann,“Knowledge-based augmented reality,”Commu-5nications of the ACM,vol.36,pp.52–62,July1993.[5]B.Bell,S.Feiner and T.H¨o llerer,“View manage-ment for virtual and augmented reality,”in Proc.ACMUIST2001(Symp.on User Interface Software andTechnology),pp.101–110,ACM Press,2001.[6]B.MacIntyre,E.Coelho and S.Julier,“Estimatingand adapting to registration errors in augmented real-ity systems,”in Proc.IEEE Conferece on Virtual Re-ality,(Orlando,FL,USA),IEEE Press,March2002.6。