This document provides an overview of commands for changing, deleting, copying, and advanced functions in the vi text editor. It lists commands for changing text like cw to change a word or r to replace a character. Deleting commands include x to delete a character, dd to delete a line, and D to delete from the cursor to the end of the line. Copy and paste is done with yy to copy a line and p to paste. Advanced commands allow joining lines with J, shifting text with < and >, toggling case with ~ and viewing file info with ^G.
Machine-Independent Optimizations: The Principal Sources of Optimization, Introduction to Data-Flow Analysis, Foundations of Data-Flow Analysis, Constant Propagation, Partial Redundancy Elimination, Loops in Flow Graphs
Big Data Sources PowerPoint Presentation Slides SlideTeam
Manage the big data efficiently using our big data sources PowerPoint deck. These 25 slides presentation deck gives you the access to highlight the information related to the most complex topic in the most desired manner. This PPT deck has been designed by our creative and proficient designers who have great designing skills and knowledge about the concept of big data. Our PPT deck helps you make your people understand that from where the data comes and where it gets stored. Every individual today is using one or another device to accomplish their day to day tasks but only few know how they can assure to protect their data and how it is saved in the cloud storage. To make a presentation where you have to share the information about big data sources you can use this big data sources PowerPoint deck. Some key presentation slides that are included in the deck are cloud, web, internet of things, media, databases, data warehouse appliances etc. Disprove exaggerated claims with our Big Data Sources PowerPoint Presentation Slides. Force the boastful to accept their error.
Abstract: This PDSG workship introduces basic concepts on Greedy and A-STAR search. Examples are given pictorially, as pseudo code and in Python.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we’ll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we’ll first see how to do direct image recognition. We’ll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don’t.
In recent years due to advancement in video and image editing tools
it has become increasingly easy to modify the multimedia content. The
doctored videos are very difficult to identify through visual
examination as artifacts left behind by processing steps are subtle
and cannot be easily captured visually. Therefore, the integrity of
digital videos can no longer be taken for granted and these are not
readily acceptable as a proof-of-evidence in court-of-law. Hence,
identifying the authenticity of videos has become an important field
of information security.
In this thesis work, we present a novel approach to detect and
temporally localize video inpainting forgery based on optical flow
consistency. The proposed algorithm comprises of two stages. In the
first step, we detect if the given video is inpainted or authentic and
in the second step we perform temporal localization. Towards this, we
first compute the optical flow between frames. Further, we analyze the
goodness of fit of chi-square values obtained from optical flow
histograms using a Guassian mixture model. A threshold is then applied
to classify between authentic and inpainted videos. In the next step,
we extract Transition Probability Matrices (TPMs) by modelling the
optical flow as first order Markov process. SVM based classification
is then applied on the obtained TPM features to decide whether a block
of non-overlapping frames is authentic or inpainted thus obtaining
temporal localization. In order to evaluate the robustness of the
proposed algorithm, we perform the experiments against two popular and
efficient inpainting techniques. We test our algorithm on public
datasets like PETS and SULFA. The results show that the approach is
effective against the inpainting techniques. In addition, it detects
and localizes the inpainted frames in a video with high accuracy and
low false positives.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The vi editor (short for visual editor) is a screen editor which is available on almost all Unix systems. Once you have learned vi, you will find that it is a fast and powerful editor. vi has no menus but instead uses combinations of keystrokes in order to accomplish commands.
Machine-Independent Optimizations: The Principal Sources of Optimization, Introduction to Data-Flow Analysis, Foundations of Data-Flow Analysis, Constant Propagation, Partial Redundancy Elimination, Loops in Flow Graphs
Big Data Sources PowerPoint Presentation Slides SlideTeam
Manage the big data efficiently using our big data sources PowerPoint deck. These 25 slides presentation deck gives you the access to highlight the information related to the most complex topic in the most desired manner. This PPT deck has been designed by our creative and proficient designers who have great designing skills and knowledge about the concept of big data. Our PPT deck helps you make your people understand that from where the data comes and where it gets stored. Every individual today is using one or another device to accomplish their day to day tasks but only few know how they can assure to protect their data and how it is saved in the cloud storage. To make a presentation where you have to share the information about big data sources you can use this big data sources PowerPoint deck. Some key presentation slides that are included in the deck are cloud, web, internet of things, media, databases, data warehouse appliances etc. Disprove exaggerated claims with our Big Data Sources PowerPoint Presentation Slides. Force the boastful to accept their error.
Abstract: This PDSG workship introduces basic concepts on Greedy and A-STAR search. Examples are given pictorially, as pseudo code and in Python.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we’ll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we’ll first see how to do direct image recognition. We’ll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don’t.
In recent years due to advancement in video and image editing tools
it has become increasingly easy to modify the multimedia content. The
doctored videos are very difficult to identify through visual
examination as artifacts left behind by processing steps are subtle
and cannot be easily captured visually. Therefore, the integrity of
digital videos can no longer be taken for granted and these are not
readily acceptable as a proof-of-evidence in court-of-law. Hence,
identifying the authenticity of videos has become an important field
of information security.
In this thesis work, we present a novel approach to detect and
temporally localize video inpainting forgery based on optical flow
consistency. The proposed algorithm comprises of two stages. In the
first step, we detect if the given video is inpainted or authentic and
in the second step we perform temporal localization. Towards this, we
first compute the optical flow between frames. Further, we analyze the
goodness of fit of chi-square values obtained from optical flow
histograms using a Guassian mixture model. A threshold is then applied
to classify between authentic and inpainted videos. In the next step,
we extract Transition Probability Matrices (TPMs) by modelling the
optical flow as first order Markov process. SVM based classification
is then applied on the obtained TPM features to decide whether a block
of non-overlapping frames is authentic or inpainted thus obtaining
temporal localization. In order to evaluate the robustness of the
proposed algorithm, we perform the experiments against two popular and
efficient inpainting techniques. We test our algorithm on public
datasets like PETS and SULFA. The results show that the approach is
effective against the inpainting techniques. In addition, it detects
and localizes the inpainted frames in a video with high accuracy and
low false positives.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
The vi editor (short for visual editor) is a screen editor which is available on almost all Unix systems. Once you have learned vi, you will find that it is a fast and powerful editor. vi has no menus but instead uses combinations of keystrokes in order to accomplish commands.
report of dance, drama and music academy and auditoriumShourya Puri
case study of triveni kala sangam and kala kendra, goa.
site analysis of gurugram
literature study of dance, drama and music studio and auditorium
about gurugram
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
3. Change Commands
• The capability to change characters, words, or lines in vi
without deleting them.
• The relevant commands are:
3
Command Description
cc Removes contents of the line, leaving you in insert mode.
cw Changes the word the cursor is on from the cursor to the lowercase w end of the word.
r Replaces the character under the cursor. vi returns to command mode after the replacement is entered.
R
Overwrites multiple characters beginning with the character currently under the cursor. You must use Esc to
stop the overwriting.
s Replaces the current character with the character you type. Afterward, you are left in insert mode.
S
Deletes the line the cursor is on and replaces with new text. After the new text is entered, vi remains in insert
mode.
4. Deleting Commands
• It’s an important commands which can be used to delete
characters and lines in an opened file.
• The relevant commands are:
4
Command Description
x Deletes the character under the cursor location.
X Deletes the character before the cursor location.
dw Deletes from the current cursor location to the next word.
d^ Deletes from current cursor position to the beginning of the line.
d$ Deletes from current cursor position to the end of the line.
D Deletes from the cursor position to the end of the current line.
dd Deletes the line the cursor is on.
NOTE:- As mentioned above, most commands in vi can be prefaced by the number of times you want the action
to occur. For example, 2x deletes two character under the cursor location and 2dd deletes two lines the cursor is
on.
6. Copy and Paste Commands
• We can copy lines or words from one place and then We
can past them at another place.
• The relevant commands are:
6
Command Description
yy Copies the current line.
yw Copies the current word from the character the lowercase w cursor is on until the end of the word.
p Puts the copied text after the cursor.
P Puts the yanked text before the cursor.
7. Advance commands
• There are some advanced commands that simplify day-
to-day editing and allow for more efficient use of vi:
7
Command Description
J Join the current line with the next one. A count joins that many lines.
<< Shifts the current line to the left by one shift width.
>> Shifts the current line to the right by one shift width.
~ Switch the case of the character under the cursor.
^G Press CNTRL and G keys at the same time to show the current filename and the status.
U Restore the current line to the state it was in before the cursor entered the line.
u Undo the last change to the file. Typing 'u' again will re-do the change.
8. Advance commands
J Join the current line with the next one. A count joins that many lines.
:f Displays current position in the file in % and file name, total number of file.
:f filename Renames current file to filename.
:w filename Write to file filename.
:e filename Opens another file with filename.
:cd dirname Changes current working directory to dirname.
:e # Use to toggle between two opened files.
:n In case you open multiple files using vi, use :n to go to next file in the series.
:p
In case you open multiple files using vi, use :p to go to previous file in the
series.
:N
In case you open multiple files using vi, use :N to go to previous file in the
series.
:r file Reads file and inserts it after current line
:nr file Reads file and inserts it after line n.
8