This document summarizes various plagiarism detection techniques. It discusses detecting plagiarism in documents using web-enabled systems like Turnitin and SafeAssign or stand-alone systems like EVE and WCopyFind. It also covers detecting plagiarism in computer code using structure-based methods like Plague, YAP, and JPlag. Common plagiarism techniques discussed include string tiling and parse tree comparison. Algorithms are based on string comparisons and handle different levels of code modification. Existing tools use fingerprints, stylometry, or integrate search APIs to detect plagiarism.
A Review Of Plagiarism Detection Based On Lexical And Semantic ApproachCourtney Esco
This is a free online plagiarism
checker and citation assistant created by Anthropic. It
checks submitted text against its own large database of
content scraped from websites. The tool provides a report
on any matching text and highlights the degree of overlap.
How can you check plagiarism in your thesis? In this PPT I am going to tell you about Thesis plagiarism checking and removing. For more details check out the given the links...
This document summarizes several methods for detecting plagiarism between documents, including the Winnowing algorithm, Rabin-Karp algorithm, and Levenshtein algorithm. It discusses how each algorithm works by extracting and comparing features from the text, such as n-grams, hashes, and edit distances. The goal of these algorithms is to determine the percentage similarity between two documents to identify potential plagiarism.
A REPORT On DETECTION OF PHISHING WEBSITE USING MACHINE LEARNINGEmma Burke
1. The document describes a project to detect phishing websites using machine learning. It discusses using k-means clustering algorithms and features like URLs and domain information to classify websites as legitimate or phishing.
2. A web application was developed with a front-end GUI and a machine learning model as the back-end server. The model analyzes URLs and identifies them as legitimate or phishing sites.
3. Python, NumPy, scikit-learn, and WHOIS databases are used as tools in the detection model to classify websites based on URL and domain features. Screenshots show examples of the web app identifying phishing and legitimate URLs.
IRJET - Online Assignment Plagiarism Checking using Data Mining and NLPIRJET Journal
This document presents a proposed system for detecting plagiarism in student assignments submitted online. The system would use data mining algorithms and natural language processing to compare submitted assignments against each other and identify plagiarized content. It would analyze assignments at both the syntactic and semantic levels. The proposed system is intended to more efficiently and accurately detect plagiarism compared to teachers manually reviewing all submissions. The document describes the workflow of the system, including preprocessing of assignments, text analysis, similarity measurement, and algorithms that would be used like Rabin-Karp, KMP and SCAM.
This document summarizes various plagiarism detection techniques. It discusses detecting plagiarism in documents using web-enabled systems like Turnitin and SafeAssign or stand-alone systems like EVE and WCopyFind. It also covers detecting plagiarism in computer code using structure-based methods like Plague, YAP, and JPlag. Common plagiarism techniques discussed include string tiling and parse tree comparison. Algorithms are based on string comparisons and handle different levels of code modification. Existing tools use fingerprints, stylometry, or integrate search APIs to detect plagiarism.
A Review Of Plagiarism Detection Based On Lexical And Semantic ApproachCourtney Esco
This is a free online plagiarism
checker and citation assistant created by Anthropic. It
checks submitted text against its own large database of
content scraped from websites. The tool provides a report
on any matching text and highlights the degree of overlap.
How can you check plagiarism in your thesis? In this PPT I am going to tell you about Thesis plagiarism checking and removing. For more details check out the given the links...
This document summarizes several methods for detecting plagiarism between documents, including the Winnowing algorithm, Rabin-Karp algorithm, and Levenshtein algorithm. It discusses how each algorithm works by extracting and comparing features from the text, such as n-grams, hashes, and edit distances. The goal of these algorithms is to determine the percentage similarity between two documents to identify potential plagiarism.
A REPORT On DETECTION OF PHISHING WEBSITE USING MACHINE LEARNINGEmma Burke
1. The document describes a project to detect phishing websites using machine learning. It discusses using k-means clustering algorithms and features like URLs and domain information to classify websites as legitimate or phishing.
2. A web application was developed with a front-end GUI and a machine learning model as the back-end server. The model analyzes URLs and identifies them as legitimate or phishing sites.
3. Python, NumPy, scikit-learn, and WHOIS databases are used as tools in the detection model to classify websites based on URL and domain features. Screenshots show examples of the web app identifying phishing and legitimate URLs.
IRJET - Online Assignment Plagiarism Checking using Data Mining and NLPIRJET Journal
This document presents a proposed system for detecting plagiarism in student assignments submitted online. The system would use data mining algorithms and natural language processing to compare submitted assignments against each other and identify plagiarized content. It would analyze assignments at both the syntactic and semantic levels. The proposed system is intended to more efficiently and accurately detect plagiarism compared to teachers manually reviewing all submissions. The document describes the workflow of the system, including preprocessing of assignments, text analysis, similarity measurement, and algorithms that would be used like Rabin-Karp, KMP and SCAM.
IRJET- An Effective Analysis of Anti Troll System using Artificial Intell...IRJET Journal
This document discusses various techniques for detecting trolls using artificial intelligence and machine learning. It first reviews related work on sentiment analysis, supervised machine learning for troll detection, real-time sentiment analysis, and analyzing vulnerabilities in social networks. It then analyzes the limitations of current troll detection systems and how AI/ML solutions can help overcome these. The literature survey covers key approaches used for troll detection, including sentiment analysis, supervised learning models, and analyzing post vulnerabilities.
IRJET- Fake News Detection using Logistic RegressionIRJET Journal
1) The document discusses a study that uses logistic regression to classify news articles as real or fake. It outlines the methodology which includes data preprocessing, feature extraction using bag-of-words and TF-IDF, and using a logistic regression classifier to predict fake news.
2) The model achieved an accuracy of approximately 72% at classifying news as real or fake when using TF-IDF features and logistic regression.
3) The study aims to address the growing issue of fake news proliferation online by developing a computational method for identifying unreliable news sources.
The Plagiarism Detection Systems for Higher Education - A Case Study in Saudi...ijseajournal
Plagiarism, cheating, and other types of academic misconduct are critical issues in higher education. In
this study, we conducted two questionnaires, one for Saudi universities and another for Saudi students at
different Saudi universities to investigate their beliefs and perceptions about plagiarism tools.
The first questionnaire was conducted to investigate to which degree the Saudi universities use plagiarism
tools. Four universities responded to our questionnaire (KSU, Immamu, PSAU, and Shaqra University).
The second questionnaire was used to investigate the user perceptions toward plagiarism tools in their
universities. Forty students responded to this questionnaire. Each student was answered 20 questions. Part
of these questions is to measure the confidence of students in terms of referencing; another part is to
measure the overall confidence to the system and evaluation of the students’ experience. The responses
indicate that the respondents believe that some questions reflect their own cases with the plagiarism during
their educational lifetime.
We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed
similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.
Review of plagiarism detection and control & copyrights in Indiaijiert bestjournal
Plagiarism software�s has been in use for almost a decade to get the sense of �theft of intellectual property". However,in today�s digital world the easy access to the web,lar ge databases,and telecommunication in general,has turned plagiarism into a serious problem for publishers,researc hers and educational institutions. The first part of this paper discusses the different plagiarism detection techni ques such as text based,citation based and shape based and compared with respect of their features and performance and an overview of different plagiarism detection methods used for text documents have taken. The second half of the paper is fully dedicated to copyrights in India.
OPEN SOURCE TECHNOLOGY: AN EMERGING AND VITAL PARADIGM IN INSTITUTIONS OF LEA...ijcsit
Open Source Software is the major rival in the software market previously dominated by proprietary software products. Open Source Software(OSS) is available in various forms including web servers, Enterprise Resource Planning systems (ERPs), Academic management systems and network management systems and the development and uptake of such software by both commercial and non-commercial companies and institutions is still on the rise. The availability of OSS applications for every common type of enterprise, minimal licensing issues and availability of source code as well as ease of access has made the technology even more attractive in learning and teaching of software based courses in institutions of learning. Through embracing this technology, institutions of learning have been able to minimize general operations cost that could have otherwise been incurred in procuring similar proprietary software. Students and teaching staff can nowadays interact and modify the readily available source code hence making learning and teaching more practical
This document discusses the use of open source technology in institutions of learning in Kenya. It finds that students and teaching staff widely use open source software and tools in learning and teaching due to factors like ease of access, lack of vendor dependency, and enhancement of the learning process. Open source allows students to access source codes and modify software, supporting the learning of software development skills. Institutions also benefit from the flexibility and cost-effectiveness of open source. The study concludes that open source has become an important part of learning and operations in Kenyan educational institutions.
A STUDY ON PLAGIARISM CHECKING WITH APPROPRIATE ALGORITHM IN DATAMININGAllison Thompson
This document summarizes a research paper that aims to improve plagiarism detection algorithms. The paper seeks to develop techniques that can identify plagiarized text even when it has been paraphrased or rewritten using synonyms. It discusses how current plagiarism detection systems are too slow and rely only on lexical structure rather than semantic structure. The paper will explore using semantic role labeling and other new techniques to handle plagiarism at the semantic level to more accurately detect plagiarized content. The objective is to efficiently find plagiarized content between documents, even when the meaning and concepts are similar but not identical.
Malicious-URL Detection using Logistic Regression TechniqueDr. Amarjeet Singh
Over the last few years, the Web has seen a
massive growth in the number and kinds of web services.
Web facilities such as online banking, gaming, and social
networking have promptly evolved as has the faith upon them
by people to perform daily tasks. As a result, a large amount
of information is uploaded on a daily to the Web. As these
web services drive new opportunities for people to interact,
they also create new opportunities for criminals. URLs are
launch pads for any web attacks such that any malicious
intention user can steal the identity of the legal person by
sending the malicious URL. Malicious URLs are a keystone
of Internet illegitimate activities. The dangers of these sites
have created a mandates for defences that protect end-users
from visiting them. The proposed approach is that classifies
URLs automatically by using Machine-Learning algorithm
called logistic regression that is used to binary classification.
The classifiers achieves 97% accuracy by learning phishing
URLs
Predicting cyber bullying on t witter using machine learningMirXahid1
The document discusses a project that aims to predict cyberbullying on Twitter using machine learning. The objectives are to use natural language processing techniques like sentiment analysis and part-of-speech tagging on tweets to extract features and train a machine learning model using these features to predict if a tweet contains cyberbullying or not. The proposed system collects tweets, converts emojis and audio to text, preprocesses the data by removing stop words and punctuation, and trains a recurrent neural network model for prediction. The current status is that the team has collected datasets and is working with them. Hardware and software requirements for the project are also outlined.
The document discusses the differences and similarities between open source and open data. Open source refers to software where the source code is openly available, while open data refers to freely available data that can be used and shared by anyone. Both open source and open data aim for transparency and collaboration. However, open source focuses on programs and code, while open data focuses on freely sharing raw data for any purpose. Laws and adoption have also progressed further for open data compared to open source. Overall, the goals of openness are largely aligned between the two concepts.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Artificial intelligence and Public health(reading based ppt).pptxsarojrimal7
This study explored how an AI chatbot (GPT-3) could contribute to public health research. The researchers engaged in a dialogue with GPT-3 to understand how it could help with tasks like summarizing data, generating reports, and developing educational materials. While GPT-3 provided some useful insights, its generated references were fabricated. The study concludes that policies are needed to ensure AI contributions to research uphold scientific standards and integrity.
The document discusses software piracy in Bangladesh. It begins with an abstract that notes software piracy poses a threat to Bangladesh's growing software industry and rates of piracy are highest among college students. A survey and interviews were conducted to understand reasons for high piracy rates. The analysis found low incomes, high software prices, lack of awareness, and not understanding effects of piracy are reasons for software piracy in Bangladesh. The document then covers background topics on software piracy including types of piracy and rates among college students. It aims to identify factors driving piracy and solutions to reduce piracy in Bangladesh.
WAYS OF HANDLING DIFFERENT TYPES OF FABRICS.pptxNicaMoreno
The document discusses different types of fabrics and how to handle them. It defines what fabric is and lists some common natural and synthetic fiber types. The document notes that each fiber has unique properties, with some being sturdy and thick while others are smooth and flexible. It provides links to external sources about different fabric types and care tips.
This document summarizes a systematic literature review that developed a taxonomy of human errors in software requirements engineering. The review identified types of human errors described in software engineering and psychology literature. It organized these errors into a "Human Error Taxonomy" based on James Reason's well-known taxonomy of slips, lapses, and mistakes from cognitive psychology. The taxonomy is intended to help requirements engineers understand and prevent common human errors during requirements development in order to improve software quality.
Sentiment Analysis in Social Media and Its OperationsIRJET Journal
This document summarizes a literature review on sentiment analysis in social media. It explores the styles, platforms, and applications of sentiment analysis. Most papers used either a dictionary-based approach or machine learning approach to analyze sentiment in social media text, with some combining both. Twitter was the most common social media platform used to collect data due to its large volume of public posts. Sentiment analysis has been applied in various domains including business, politics, health, and tracking world events. It can provide valuable insights for organizations and help improve products, services, and decision making.
The document discusses similarity checker applications, which are tools used to help identify similarities between submitted works and sources available online or other databases. It provides examples of common similarity checker applications like Turnitin, iThenticate, Plagiarism Checker X, and Grammarly. It also outlines tips for choosing a similarity checker application, such as accuracy, availability, usability, capabilities, cost, and privacy.
https://www.ted.com/talks/shyam_sankar_the_rise_of_human_computer_cooperation
http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=v4BFIWPrO5n
http://www.research.ibm.com/client-programs/index.shtml
https://www.ibm.com/design/
https://www.youtube.com/watch?v=RHMl5SHHYjM
2/26/17, 3)10 PM
Page 1 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Module 2 - SLP
BUSINESS AND ORGANIZATIONAL ASPECTS OF HCI
For this exercise, please return to the software package that you used in your
SLP assignment for Module 1.
SLP Assignment Expectations
Then please prepare a paper addressing these topics.
Have you ever called their technical support to get help due to lack of ease of
use? Why or why not?
What more you would like to have in the software from an ease of use point of
view?
Would you be willing to pay more for the software for such features? Why or
why not?
Any conclusions you might have drawn about ease of use as a business
criterion, and why you make this assessment.
SLP Grading and Expectations
Your paper will be evaluated on the following criteria:
Complete the SLP assignment. Length of 2-3 pages (since a page is about 300
words, this is approximately 600-900 words)
Conducted evaluation and analysis as required
Precision: the questions asked are answered.
Clarity: Your answers are clear and show your good understanding of the
topic.
Breadth and Depth: The scope covered in your paper is directly related to the
questions of the assignment and the learning objectives of the module.
Listen
https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1/DW4Mod%20-%20Codes/EMPTY%204-MODULE%20HTML%20DOCS/Modules/Module2/https%3A%2F%2Fapp.readspeaker.com%2Fcgi-bin%2Frsent?customerid=8725&lang=en_us&voice=Kate&readid=d2l_read_element&url=https%3A%2F%2Ftlc.trident.edu%2Fcontent%2Fenforced%2F85341-ITM433-FEB2017FT-1%2FDW4Mod%2520-%2520Codes%2FEMPTY%25204-MODULE%2520HTML%2520DOCS%2FModules%2FModule2%2FMod2SLP.html
2/26/17, 3)10 PM
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Privacy Policy | Contact
Critical thinking: Incorporate YOUR reactions, examples, and applications of
the material to business that illustrate your reflective judgment and good
understanding of the concepts.
Your paper is well written and the references are properly cited and listed
http://www.trident.edu/privacy-policy
http://www.trident.edu/university-information/contact-us
Search HFES.com
Think Again Before Tapping the Install Button for That App
Friday, October 16, 2015
Before installing a new app on a mobile device, people need to be mindful of the security
risks. One poor decision can bypass the most secure encryption, and a malicious app can
gain access to confidential information or even lock the user’s device. A presentation at
the upcoming HF ...
IRJET- An Effective Analysis of Anti Troll System using Artificial Intell...IRJET Journal
This document discusses various techniques for detecting trolls using artificial intelligence and machine learning. It first reviews related work on sentiment analysis, supervised machine learning for troll detection, real-time sentiment analysis, and analyzing vulnerabilities in social networks. It then analyzes the limitations of current troll detection systems and how AI/ML solutions can help overcome these. The literature survey covers key approaches used for troll detection, including sentiment analysis, supervised learning models, and analyzing post vulnerabilities.
IRJET- Fake News Detection using Logistic RegressionIRJET Journal
1) The document discusses a study that uses logistic regression to classify news articles as real or fake. It outlines the methodology which includes data preprocessing, feature extraction using bag-of-words and TF-IDF, and using a logistic regression classifier to predict fake news.
2) The model achieved an accuracy of approximately 72% at classifying news as real or fake when using TF-IDF features and logistic regression.
3) The study aims to address the growing issue of fake news proliferation online by developing a computational method for identifying unreliable news sources.
The Plagiarism Detection Systems for Higher Education - A Case Study in Saudi...ijseajournal
Plagiarism, cheating, and other types of academic misconduct are critical issues in higher education. In
this study, we conducted two questionnaires, one for Saudi universities and another for Saudi students at
different Saudi universities to investigate their beliefs and perceptions about plagiarism tools.
The first questionnaire was conducted to investigate to which degree the Saudi universities use plagiarism
tools. Four universities responded to our questionnaire (KSU, Immamu, PSAU, and Shaqra University).
The second questionnaire was used to investigate the user perceptions toward plagiarism tools in their
universities. Forty students responded to this questionnaire. Each student was answered 20 questions. Part
of these questions is to measure the confidence of students in terms of referencing; another part is to
measure the overall confidence to the system and evaluation of the students’ experience. The responses
indicate that the respondents believe that some questions reflect their own cases with the plagiarism during
their educational lifetime.
We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed
similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.
Review of plagiarism detection and control & copyrights in Indiaijiert bestjournal
Plagiarism software�s has been in use for almost a decade to get the sense of �theft of intellectual property". However,in today�s digital world the easy access to the web,lar ge databases,and telecommunication in general,has turned plagiarism into a serious problem for publishers,researc hers and educational institutions. The first part of this paper discusses the different plagiarism detection techni ques such as text based,citation based and shape based and compared with respect of their features and performance and an overview of different plagiarism detection methods used for text documents have taken. The second half of the paper is fully dedicated to copyrights in India.
OPEN SOURCE TECHNOLOGY: AN EMERGING AND VITAL PARADIGM IN INSTITUTIONS OF LEA...ijcsit
Open Source Software is the major rival in the software market previously dominated by proprietary software products. Open Source Software(OSS) is available in various forms including web servers, Enterprise Resource Planning systems (ERPs), Academic management systems and network management systems and the development and uptake of such software by both commercial and non-commercial companies and institutions is still on the rise. The availability of OSS applications for every common type of enterprise, minimal licensing issues and availability of source code as well as ease of access has made the technology even more attractive in learning and teaching of software based courses in institutions of learning. Through embracing this technology, institutions of learning have been able to minimize general operations cost that could have otherwise been incurred in procuring similar proprietary software. Students and teaching staff can nowadays interact and modify the readily available source code hence making learning and teaching more practical
This document discusses the use of open source technology in institutions of learning in Kenya. It finds that students and teaching staff widely use open source software and tools in learning and teaching due to factors like ease of access, lack of vendor dependency, and enhancement of the learning process. Open source allows students to access source codes and modify software, supporting the learning of software development skills. Institutions also benefit from the flexibility and cost-effectiveness of open source. The study concludes that open source has become an important part of learning and operations in Kenyan educational institutions.
A STUDY ON PLAGIARISM CHECKING WITH APPROPRIATE ALGORITHM IN DATAMININGAllison Thompson
This document summarizes a research paper that aims to improve plagiarism detection algorithms. The paper seeks to develop techniques that can identify plagiarized text even when it has been paraphrased or rewritten using synonyms. It discusses how current plagiarism detection systems are too slow and rely only on lexical structure rather than semantic structure. The paper will explore using semantic role labeling and other new techniques to handle plagiarism at the semantic level to more accurately detect plagiarized content. The objective is to efficiently find plagiarized content between documents, even when the meaning and concepts are similar but not identical.
Malicious-URL Detection using Logistic Regression TechniqueDr. Amarjeet Singh
Over the last few years, the Web has seen a
massive growth in the number and kinds of web services.
Web facilities such as online banking, gaming, and social
networking have promptly evolved as has the faith upon them
by people to perform daily tasks. As a result, a large amount
of information is uploaded on a daily to the Web. As these
web services drive new opportunities for people to interact,
they also create new opportunities for criminals. URLs are
launch pads for any web attacks such that any malicious
intention user can steal the identity of the legal person by
sending the malicious URL. Malicious URLs are a keystone
of Internet illegitimate activities. The dangers of these sites
have created a mandates for defences that protect end-users
from visiting them. The proposed approach is that classifies
URLs automatically by using Machine-Learning algorithm
called logistic regression that is used to binary classification.
The classifiers achieves 97% accuracy by learning phishing
URLs
Predicting cyber bullying on t witter using machine learningMirXahid1
The document discusses a project that aims to predict cyberbullying on Twitter using machine learning. The objectives are to use natural language processing techniques like sentiment analysis and part-of-speech tagging on tweets to extract features and train a machine learning model using these features to predict if a tweet contains cyberbullying or not. The proposed system collects tweets, converts emojis and audio to text, preprocesses the data by removing stop words and punctuation, and trains a recurrent neural network model for prediction. The current status is that the team has collected datasets and is working with them. Hardware and software requirements for the project are also outlined.
The document discusses the differences and similarities between open source and open data. Open source refers to software where the source code is openly available, while open data refers to freely available data that can be used and shared by anyone. Both open source and open data aim for transparency and collaboration. However, open source focuses on programs and code, while open data focuses on freely sharing raw data for any purpose. Laws and adoption have also progressed further for open data compared to open source. Overall, the goals of openness are largely aligned between the two concepts.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Artificial intelligence and Public health(reading based ppt).pptxsarojrimal7
This study explored how an AI chatbot (GPT-3) could contribute to public health research. The researchers engaged in a dialogue with GPT-3 to understand how it could help with tasks like summarizing data, generating reports, and developing educational materials. While GPT-3 provided some useful insights, its generated references were fabricated. The study concludes that policies are needed to ensure AI contributions to research uphold scientific standards and integrity.
The document discusses software piracy in Bangladesh. It begins with an abstract that notes software piracy poses a threat to Bangladesh's growing software industry and rates of piracy are highest among college students. A survey and interviews were conducted to understand reasons for high piracy rates. The analysis found low incomes, high software prices, lack of awareness, and not understanding effects of piracy are reasons for software piracy in Bangladesh. The document then covers background topics on software piracy including types of piracy and rates among college students. It aims to identify factors driving piracy and solutions to reduce piracy in Bangladesh.
WAYS OF HANDLING DIFFERENT TYPES OF FABRICS.pptxNicaMoreno
The document discusses different types of fabrics and how to handle them. It defines what fabric is and lists some common natural and synthetic fiber types. The document notes that each fiber has unique properties, with some being sturdy and thick while others are smooth and flexible. It provides links to external sources about different fabric types and care tips.
This document summarizes a systematic literature review that developed a taxonomy of human errors in software requirements engineering. The review identified types of human errors described in software engineering and psychology literature. It organized these errors into a "Human Error Taxonomy" based on James Reason's well-known taxonomy of slips, lapses, and mistakes from cognitive psychology. The taxonomy is intended to help requirements engineers understand and prevent common human errors during requirements development in order to improve software quality.
Sentiment Analysis in Social Media and Its OperationsIRJET Journal
This document summarizes a literature review on sentiment analysis in social media. It explores the styles, platforms, and applications of sentiment analysis. Most papers used either a dictionary-based approach or machine learning approach to analyze sentiment in social media text, with some combining both. Twitter was the most common social media platform used to collect data due to its large volume of public posts. Sentiment analysis has been applied in various domains including business, politics, health, and tracking world events. It can provide valuable insights for organizations and help improve products, services, and decision making.
The document discusses similarity checker applications, which are tools used to help identify similarities between submitted works and sources available online or other databases. It provides examples of common similarity checker applications like Turnitin, iThenticate, Plagiarism Checker X, and Grammarly. It also outlines tips for choosing a similarity checker application, such as accuracy, availability, usability, capabilities, cost, and privacy.
https://www.ted.com/talks/shyam_sankar_the_rise_of_human_computer_cooperation
http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=v4BFIWPrO5n
http://www.research.ibm.com/client-programs/index.shtml
https://www.ibm.com/design/
https://www.youtube.com/watch?v=RHMl5SHHYjM
2/26/17, 3)10 PM
Page 1 of 2https://tlc.trident.edu/content/enforced/85341-ITM433-FEB2017FT-1…d2lSessionVal=fHIzkKJBP8wWlfc2loiXUyRDM&ou=85341&d2l_body_type=3
Module 2 - SLP
BUSINESS AND ORGANIZATIONAL ASPECTS OF HCI
For this exercise, please return to the software package that you used in your
SLP assignment for Module 1.
SLP Assignment Expectations
Then please prepare a paper addressing these topics.
Have you ever called their technical support to get help due to lack of ease of
use? Why or why not?
What more you would like to have in the software from an ease of use point of
view?
Would you be willing to pay more for the software for such features? Why or
why not?
Any conclusions you might have drawn about ease of use as a business
criterion, and why you make this assessment.
SLP Grading and Expectations
Your paper will be evaluated on the following criteria:
Complete the SLP assignment. Length of 2-3 pages (since a page is about 300
words, this is approximately 600-900 words)
Conducted evaluation and analysis as required
Precision: the questions asked are answered.
Clarity: Your answers are clear and show your good understanding of the
topic.
Breadth and Depth: The scope covered in your paper is directly related to the
questions of the assignment and the learning objectives of the module.
Listen
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Plagiarism Preventive Initiatives and AI Tools......docx
1. 1
Author: DEEN SYEEDIN
NORTHERN UNIVERSITY,BANGLADESH
.-----------------------------------------------------------
Paper Name: Plagiarism and AI Tools,
“How Technologically Updated Tools of Software Increase the
Risks of Plagiarism”
Main Theme of the Paper:
How plagiarism can be prevented while keeping pace with the
most competitive AI technology's newly updated features that is
the main theme of the article.
Today’s era is of advanced information technology and, the
availability of free information is of world folk’s grab that’s why
misuse of it in different sectors /platforms by people has occurred.
2. 2
The invention of the worldwide networking system has ensured the
freedom of the incessant availability of Information Technology's
accessibility for the world’s people.
Research Methodology:
In two ways, my research methodology has been completed. First
of all, I have found which initiatives are preventive to thwart
plagiarism, and, which reasons, these plagiarism preventive
initiatives are being intensified by AI based updated features. Both
of these are the main objectives of my research methods.
Hypothesis:
AI technology's newly updated features intensify plagiarism on
academic platforms.
3. 3
Findings Area:
AI based application software’ companies’ developers shall work
with those plagiarism augmented tools and features that will help
the reduction of plagiarism. So, behind companies of the software
programmers, a high amount of money shall be invested.
Limitations:
Literature review, data collection and visualization are the
limitations of my research.
Main Manuscript
Definition of Plagiarism
Countless servers under the computer networking system based on
the internet contain countless websites in which information of
written documents, audio and video files, and pictures of various
4. 4
formats) are stored, and those are of authentic proprietors. When
those possessions are used without the authors/proprietors'
permission by others that act is plagiarism.
Plagiarism and the Violation of Computer Ethics:
However, this kind of act is a violation of computer ethics, the
unfortunate fact is that this kind of activity practice is frightful and
the evidence of it is visible in almost every sector. Although to
prevent this kind of heinous act, there are advanced technological
advantages.
To take any idea and use it as own derived from any written
document on the Internet without the permission of the main
author will be considered plagiarism.
Sorts of Plagiarism:
5. 5
To use any image(Graphics,3D images)from the Internet
without mentioning the reference of the link of the image
will be considered plagiarism.
To take a thesis and a research paper from the Internet not to
mention the source of those papers, and their authentic
author's name will come under plagiarism.
Not to refer to the name of the source of the data of statistics
taken from the internet will go under plagiarism. Likewise,
quotes derived from the literary works not to refer to the
author's name and reference will go under plagiarism.
Provoking Plagiarism
6. 6
Besides, such types of made software are favorable for web-
browser like google chrome. Those software are added as an
extension in google chrome. Those software partially responsible
for provoking plagiarism. For example, Google Translate and Free
or Paid Online Grammar Checker. These two types software play a
vital role to facilitate plagiarism, no doubt.
Roles of Google for Plagiarism’s Prevention
On the other hand, to prevent plagiarism, the IT Company Giant
"Google'' has taken the preventive initiative through the launch of
"Free Plagiarism Checker Software and "Free Plagiarism Grammar
Checker Software''. There are many updated versions of these two
-software. Through these two software, it is easily identified from
which websites plagiarized contents are taken. And, through the
software "Free Plagiarism Grammar Checker Software'' it is easily
identified how many times grammatical mistakes have been
corrected.
7. 7
There are many options which are congenial to defend against
plagiarism. Those methodical initiatives have been taken against
plagiarism:
The first one is citation.
The second one is to mention reference of the source.
The third one is summarization.
The last one is Paraphrasing.
mention
reference of the
source
citation.
Paraphrasing.
8. 8
How citation process will have to complete following those kinds
of methodology.
MLA/APA formats
The written texts(whether versatile subject matters based articles,
research papers, thesis papers, journals, literary works, intellectual
written papers) published in the printed media(newspaper), in the
online platform based media which will under citation process.
In this case, index-citation and the reference of the sources of the
written texts will have to maintain following MLA/APA formats
that are acknowledged as destined rules to thwart plagiarism.
9. 9
In which types of media, the written texts will have been published
and those will have been cited, in this case, MLA/APA format will
be varied. For example: If the adapted document is cited or
referred by one from either printed media or online platform-based
media, in this case, MLA/APA format will be changed.
Summarization and Paraphrasing
Also, there are a many options to prevent plagiarism.
Summarization and Paraphrasing are the other options to evade
plagiarism, in this case, some rules will be strictly maintained.
However, in the case of summarization of the any written
document by one will concise it in a way so that own opinion-
comment-suggestion-message can never be included, otherwise, it
will go under plagiarism procedures. Only, the main information of
the adapted written text of the writer will be succinct by the
summarizer.
Paraphrasing
10. 10
Paraphrasing is another option to avoid plagiarism. Whichever
written texts are paraphrased, only, sentence structure,
synonymous-anonymous words will change. No other change
will be acceptable in the case of paraphrasing.
The main information-message-ideas of the writer of the written
text won’t be alterable, whereas, it will go under plagiarism
process.
Last but not the least, everything has good and bad sides. Our main
focus is to take good sides of everything.
“How Technologically Updated Tools of Software Increase the
Risks of Plagiarism”
11. 11
To thwart plagiarism, a large number of free and paid plagiarism
checker software programs, including Grammarly plagiarism
detection software, have been launched with the addition of new
features. During the inauguration of the e-learning system,
students’ continuous assessment and assignment papers written by
students were checked by teachers and course faculties under many
plagiarism checker applications. From which websites the articles,
essays, documents, quotes, and ideas are taken, and copy and paste
processes are applied in the answer scripts or assignments, are
checked and identified by teachers under a free or paid online
plagiarism checker software. Even though many grammar
corrections have been checked using a lot of free or paid
Grammarly plagiarism checkers by students, teachers can easily
find them using Grammarly plagiarism checking software.
However, technologically up-dated tools of software stand against
free plagiarism prevention.
12. 12
To represent the text of an article, journal, document, or essay in a
unique way, a number of free and paid Grammarly plagiarism
checkers are used to detect grammatical errors, punctuation errors,
inappropriate word selection, spelling errors, and inappropriate
sentence structures by students whose process is under plagiarism
Grammarly
plagiarism
detection
software
plagiarism
checker
software
spelling errors, and
inappropriate sentence
structures
grammatical
errors,
punctuation
errors
Disadvantages
for students
13. 13
that was identified by teachers dealing with their students
assessments.
Furthermore, there are many apps by the giant company “Google,”
like Google Docs, Google Sheets, and Google Slides. In these
three apps, the tool button provides a grammar and spelling
correction option that is defined as plagiarism. Particularly for
those who use Google Docs for writing any type of written work,
the “spelling and grammar check option’ gives them the free power
to do plagiarism. Even another technological giant, “Microsoft,”
allows its users to be involved in plagiarism. MS Word’s ‘review”
tool’s ‘spelling and grammar’ option is also responsible for
plagiarism. On MS Word, a written document/article/research
paper/manuscript/thesis paper can be checked for spelling and
grammar using the “Spelling and Grammar” option, and the
process is known as plagiarism. However, these kinds of
applications, like Google Docs, Google Slides, Google Sheets, and
MS Word, support plagiarism to a concise extent.
14. 14
Intensification of Google Applications in Plagiarism Case.
What is more, to prevent plagiarism, a number of steps were
applied. Among them are quotation, in-text citation, paraphrasing,
summarization, and the mention of references in MLA or APA
format. But these of them are not used as a criterion to detect
plagiarism in the excellence of artificial intelligence-based content
generation. There are many AI-based content generation software
products, like Chat GPT and its latest sequential versions. AI-
based contents, documents, articles, essays, and books generated
by software’s wider application and usage in all writing sectors are
through and through threats, as a matter of fact.
15. 15
How much of AI-based written documents’ accuracy and
uniqueness are determined by free or paid AI detector plagiarism
checkers, like how humans’ written documents’ authenticity and
uniqueness are ascertained by free or paid online plagiarism
checker software? Of late, there have been many AI-based written
document plagiarism detector software, for instance, Open AI Text
Classifier,GPT Radar,CopyScape, Plagibot,Writer AI Content
Detector,GPTZero,and so on. These artificial intelligence-based
AI-based written content’s generation is plagiarized or not, which
is why an AI-written document’s authenticity takes the help of an
AI plagiarism detector’s app.
However, for an AI-generated written document’s authenticity and
accuracy, AI-based plagiarism detector software is needed;
likewise, for a man-written document’s authenticity and accuracy,
a plagiarism checker and a plagiarism grammar checker software
are indispensable.
16. 16
To prevent plagiarism, those kinds of initiatives were taken, like
paraphrasing, and summarizing. But with the blessing of the
sequential advancement of IT excellence, many paraphrasing and
summarizing tools have been generated autonomously. Which
kinds of paraphrasing tools are used for it? Those names are
wordtune, quillbolt,spinbot,paraphrase.io,re-phrase.In this
case,these paraphrasing tools take double threats of plagiarism.
Also,all of these paraphrasing tools don’t become an extension of
the web browser’s sidebar. But the specific one is “Quillbot,”
which is included as an extension of Google Chrome’s sidebar and
provides four kinds of services,like paraphrase, grammar
checker,summarizer,and translator services. What is more,there are
many summarizer tools,like Quillbot, Tools4Noobs, Scribbr,
EditPad, TldrThis,and so on. AI generated summarizing tools’
usage and application in summarization-based writing platforms
can play a negative role in persuading plagiarism.
However, AI based summarizing and paraphrasing tools are the
main obstacles to dwindle plagiarism rates for academic and
research purposes. However, when those plagiarism-prevention
initiatives are taken, those initiatives soar up by AI generated tool-
based applications.
17. 17
On the whole, AI content generator tools, and applications extend
the risks of plagiarism. With much patience, the options of the
tools of the applications will keep under update procedure. If it
needs, will have to take time for new additions in the tools’ option.
So, the risks of plagiarism are increased by the novel options of the
updated tools.
Acceptance of my research’s hypothesis:
After conducting research on it, my hypothesis has come true.