The Technology of Plagiarism and Plagiarism Detection
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A look back at how technolog has changed plagiarism and created a game of cat and mouse between plagiarists and those who would wish to stop them.
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Comparing Three Plagiarism Tools (Ferret, Sherlock, and Turnitin)
Abstract An attempt was made to carry out an experiment with three plagiarism detection tools (two free/open source tools, namely, Ferret and Sherlock, and one commercial web-based software called Turnitin) on Clough-Stevenson’s corpus including documents classified in three types of plagiarism and one type of non-plagiarism. The experiment was toward Extrinsic/External detecting plagiarism. The goal was to observe the performance of the tools on the corpus and then to analyze, compare, and discuss the outputs and, finally to see whether the tools’ identification of documents is the same as that identified by Clough and Stevenson. It appeared that Ferret and Sherlock, in most cases, produce the same results in plagiarism detection performance; however, Turnitin reported the results with great difference from the other two tools: It showed a higher percentage of similarities between the documents and the source. After investigating the reason (just checked with Ferret and Turnitin, cause Sherlock does not provide a view of the two documents with the overlapped and distinct parts), it was discovered that Turnitin performs quite acceptable and it is Ferret that does not show the expected percentage; it considers the longer text (for this corpus the longer is always the source) as the base and then looks how much of this text is overlapped by the shorter text and the result is shown as the percentage of similarity between the two documents, and this leads to wrong results. From this it can be also speculated that Sherlock does not manifest the results properly.
Avoiding plagiarism in academic research
This document discusses academic research and avoiding plagiarism. It defines academic research and successful research strategies. It emphasizes the importance of citation, including giving credit to original authors, allowing readers to find sources, and avoiding penalties for plagiarism. The document outlines strategies for utilizing information, including summarizing, quoting, and paraphrasing, and provides examples of proper paraphrasing with citations. It also provides resources for MLA citation style and formatting papers.
Plagiarism Detection Tools
Slides from a short presentation given on the features of two plagiarism detection tools: Turnitin and SafeAssign at a recent Faculty Forum on Teaching and Technology (1/26/12)
for more about the Faculty Forum:
http://www.fordham.edu/campus_resources/fordham_it/help__support/faculty__staff/faculty_technology_s/faculty_forum_on_tea_78160.asp
Using Technology To Detect Plagiarism
This document discusses ways that teachers can use technology to detect plagiarism in student assignments. It notes that surveys show high rates of cheating and plagiarism among students. It then provides tips for teachers to deter plagiarism, such as educating students about proper citation and structuring assignments in multiple parts. The document recommends that teachers check assignments against in-class work, references cited, and topic appropriateness to detect plagiarism. It also suggests using online tools like Google to search suspicious phrases or subscription services that can match submissions to existing content online.
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Plagirism in chile
1. The document discusses plagiarism in advertising, defining it as using another's work without proper attribution or permission.
2. It provides examples of retail stores in Chile that have been accused of plagiarizing advertising campaigns from other countries by copying logos, slogans, designs, videos and ideas without permission.
3. The examples discussed include a store called "Paris" copying a song in a commercial from a Chilean band, a store called "Ripley" copying a video in their campaign, and the Bonella metro station copying an installation idea from Stockholm.
Casey
This document discusses plagiarism and copying others' work. It defines plagiarism as copying someone else's work and says you can get in legal trouble for plagiarism, including going to jail. The author's opinion is that while plagiarism should not be allowed, jail is too extreme a punishment. Copying others' work is also discussed, with reasons given why it should not be done, such as legal consequences and losing friends. The author's opinion on copying is that it should not be allowed because someone could copy personal information without consent.
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Abstract An attempt was made to carry out an experiment with three plagiarism detection tools (two free/open source tools, namely, Ferret and Sherlock, and one commercial web-based software called Turnitin) on Clough-Stevenson’s corpus including documents classified in three types of plagiarism and one type of non-plagiarism. The experiment was toward Extrinsic/External detecting plagiarism. The goal was to observe the performance of the tools on the corpus and then to analyze, compare, and discuss the outputs and, finally to see whether the tools’ identification of documents is the same as that identified by Clough and Stevenson. It appeared that Ferret and Sherlock, in most cases, produce the same results in plagiarism detection performance; however, Turnitin reported the results with great difference from the other two tools: It showed a higher percentage of similarities between the documents and the source. After investigating the reason (just checked with Ferret and Turnitin, cause Sherlock does not provide a view of the two documents with the overlapped and distinct parts), it was discovered that Turnitin performs quite acceptable and it is Ferret that does not show the expected percentage; it considers the longer text (for this corpus the longer is always the source) as the base and then looks how much of this text is overlapped by the shorter text and the result is shown as the percentage of similarity between the two documents, and this leads to wrong results. From this it can be also speculated that Sherlock does not manifest the results properly.
Avoiding plagiarism in academic research
This document discusses academic research and avoiding plagiarism. It defines academic research and successful research strategies. It emphasizes the importance of citation, including giving credit to original authors, allowing readers to find sources, and avoiding penalties for plagiarism. The document outlines strategies for utilizing information, including summarizing, quoting, and paraphrasing, and provides examples of proper paraphrasing with citations. It also provides resources for MLA citation style and formatting papers.
Plagiarism Detection Tools
Slides from a short presentation given on the features of two plagiarism detection tools: Turnitin and SafeAssign at a recent Faculty Forum on Teaching and Technology (1/26/12)
for more about the Faculty Forum:
http://www.fordham.edu/campus_resources/fordham_it/help__support/faculty__staff/faculty_technology_s/faculty_forum_on_tea_78160.asp
Using Technology To Detect Plagiarism
This document discusses ways that teachers can use technology to detect plagiarism in student assignments. It notes that surveys show high rates of cheating and plagiarism among students. It then provides tips for teachers to deter plagiarism, such as educating students about proper citation and structuring assignments in multiple parts. The document recommends that teachers check assignments against in-class work, references cited, and topic appropriateness to detect plagiarism. It also suggests using online tools like Google to search suspicious phrases or subscription services that can match submissions to existing content online.
Intellectual property rights
Intellectual property rights confer exclusive rights to creators and inventors over their intellectual works and creations. These rights protect various types of creations including literature, music, inventions, and more. Intellectual property law establishes these rights for a limited time to balance protection and access. Key international agreements recognize intellectual property rights, and the World Intellectual Property Organization governs these rights globally. India has established frameworks to protect various types of intellectual property rights domestically in accordance with international obligations.
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From this PPT you may get some help in finding a patent, searching a patent. hope everyone like it.
Plagirism in chile
1. The document discusses plagiarism in advertising, defining it as using another's work without proper attribution or permission.
2. It provides examples of retail stores in Chile that have been accused of plagiarizing advertising campaigns from other countries by copying logos, slogans, designs, videos and ideas without permission.
3. The examples discussed include a store called "Paris" copying a song in a commercial from a Chilean band, a store called "Ripley" copying a video in their campaign, and the Bonella metro station copying an installation idea from Stockholm.
Casey
This document discusses plagiarism and copying others' work. It defines plagiarism as copying someone else's work and says you can get in legal trouble for plagiarism, including going to jail. The author's opinion is that while plagiarism should not be allowed, jail is too extreme a punishment. Copying others' work is also discussed, with reasons given why it should not be done, such as legal consequences and losing friends. The author's opinion on copying is that it should not be allowed because someone could copy personal information without consent.
Plagiarism and its detection
This document discusses plagiarism and a program for detecting plagiarism. It begins by defining plagiarism and explaining why it is strongly discouraged. It then describes how the plagiarism detection program works by tokenizing files, comparing tokens of equal length between source and target files, and calculating plagiarism percentage. The program finds, checks, and detects plagiarism within text files. It also provides examples of penalties for plagiarism and discusses future improvements that could expand the program's capabilities to different file types and reduce runtime.
plagiarism detection tools and techniques
The document discusses various techniques for detecting plagiarism in text and source code. It defines plagiarism and describes how to avoid it through prevention and detection. For text, it covers substring matching, keyword similarity, fingerprint matching, and text parsing techniques. For source code, it discusses lexical similarities, parse trees, program dependence graphs, and metrics. It also provides examples of tools used for each type of plagiarism detection like PlagAware, MOSS, and JPlag.
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This document discusses patent databases and how to search within them. It defines what a patent is - an exclusive right granted by a government for an invention. It also defines what a database is - a collection of organized information that can be easily accessed. The document outlines several major patent databases including PatentScope from WIPO, EKASWA from India, EPIDOS-INPADOC from Europe, and the Canadian Patents Database. It provides details on how to search within each database using basic, advanced, boolean, or other specialized search features.
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The document provides an overview of patent law in India, including:
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2) India's patent law has evolved since the 1850s through several Acts, with the current Patents Act coming into force in 1970.
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Plagirism checker
The document discusses plagiarism and methods for detecting it. It defines plagiarism as passing off another's work as one's own and lists several types, including directly copying text, paraphrasing from one or multiple sources without proper citation, and borrowing from one's own previous work. It then describes an algorithmic approach to detecting plagiarism by comparing documents and text segments at the document, paragraph, and sentence levels using thresholds and word similarity techniques. WordNet and the Lesk algorithm are also referenced for analyzing word meanings and signatures to identify copied text. The document concludes by listing members of an NLP team and mentioning a demo.
Intellectual Property Rights
This document provides an overview of intellectual property (IP) including the types of IP (patents, trademarks, copyright), governing bodies and laws, and key concepts around IP rights.
1. It discusses the World Intellectual Property Organization (WIPO) which promotes IP protection worldwide and is headquartered in Geneva, Switzerland.
2. The major types of IP are outlined as functional/technical inventions protected by patents, artistic works protected by copyright, and symbols/logos protected by trademarks.
3. The rights provided by patents, copyright, and trademarks are territorial in nature and regulated by country-specific laws, though international treaties allow cross-border cooperation on IP issues.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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