Dynamic Thresholding Mechanisms for IR-Based Filtering in Efficient Source Code Plagiarism Detection

To solve time inefficiency issue, only potential pairs are compared in string-matching-based source code plagiarism detection; wherein potentiality is defined through a fast-yet-order-insensitive similarity measurement (adapted from Information Retrieval) and only pairs which similarity degrees are higher or equal to a particular threshold is selected. Defining such threshold is not a trivial task considering the threshold should lead to high efficiency improvement and low effectiveness reduction (if it is unavoidable). This paper proposes two thresholding mechanisms---namely range-based and pair-count-based mechanism---that dynamically tune the threshold based on the distribution of resulted similarity degrees. According to our evaluation, both mechanisms are more practical to be used than manual threshold assignment since they are more proportional to efficiency improvement and effectiveness reduction.Comment: The 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises

Writing History: 19th Century African American Activism

Our work in this course will center around two questions. First, what were the material and social conditions for Black men, women, and children living in the territory that would become the United States, from roughly 1750 until on or about 1860? While slavery is likely the first concept that comes to mind, additional concepts such as racism, white supremacy, and what Cedric Robinson defines as “racial capitalism” offer nuanced ways to ways to address this complicated, first question. From here, our second question, In what ways, and with what effects, did Black men, women, and children work to overcome, resist, or otherwise avail themselves from their material and social conditions during the first part of the long 19th century? points to the various successes, failures, and missed opportunities Black people had in working against white supremacy, racial capitalism and other forms of oppression. Such work came in the form of a wide variety of activism inside and outside conventional politics; from writing, speaking, and organizing to emigration and revolts. What should become clear over the arc of our course is that Black people and organizations, sometimes along with their allies in indigenous, European, and white communities, were anything but passive or unsuccessful in their work towards liberation in facing racialized power structures in the United States. HIS 2050: Writing History | 19th Century African American Activism | Fall 2018 | Florida Atlantic Universit

Choosing Code Segments to Exclude from Code Similarity Detection

When student programs are compared for similarity as a step in the detection of academic misconduct, certain segments of code are always sure to be similar but are no cause for suspicion. Some of these segments are boilerplate code (e.g. public static void main String [] args) and some will be code that was provided to students as part of the assessment specification. This working group explores these and other types of code that are legitimately common in student assessments and can therefore be excluded from similarity checking. From their own institutions, working group members collected assessment submissions that together encompass a wide variety of assessment tasks in a wide variety of programming languages. The submissions were analysed to determine what sorts of code segment arose frequently in each assessment task. The group has found that common code can arise in programming assessment tasks when it is required for compilation purposes; when it reflects an intuitive way to undertake part or all of the task in question; when it can be legitimately copied from external sources; and when it has been suggested by people with whom many of the students have been in contact. A further finding is that the nature and size of the common code fragments vary with course level and with task complexity. An informal survey of programming educators confirms the group's findings and gives some reasons why various educators include code when setting programming assignments.Peer reviewe

Structural analysis of source code plagiarism using graphs

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science. May 2017Plagiarism is a serious problem in academia. It is prevalent in the computing discipline where students are expected to submit source code assignments as part of their assessment; hence, there is every likelihood of copying. Ideally, students can collaborate with each other to perform a programming task, but it is expected that each student submit his/her own solution for the programming task. More so, one might conclude that the interaction would make them learn programming. Unfortunately, that may not always be the case. In undergraduate courses, especially in the computer sciences, if a given class is large, it would be unfeasible for an instructor to manually check each and every assignment for probable plagiarism. Even if the class size were smaller, it is still impractical to inspect every assignment for likely plagiarism because some potentially plagiarised content could still be missed by humans. Therefore, automatically checking the source code programs for likely plagiarism is essential. There have been many proposed methods that attempt to detect source code plagiarism in undergraduate source code assignments but, an ideal system should be able to differentiate actual cases of plagiarism from coincidental similarities that usually occur in source code plagiarism. Some of the existing source code plagiarism detection systems are either not scalable, or performed better when programs are modified with a number of insertions and deletions to obfuscate plagiarism. To address this issue, a graph-based model which considers structural similarities of programs is introduced to address cases of plagiarism in programming assignments. This research study proposes an approach to measuring cases of similarities in programming assignments using an existing plagiarism detection system to find similarities in programs, and a graph-based model to annotate the programs. We describe experiments with data sets of undergraduate Java programs to inspect the programs for plagiarism and evaluate the graph-model with good precision. An evaluation of the graph-based model reveals a high rate of plagiarism in the programs and resilience to many obfuscation techniques, while false detection (coincident similarity) rarely occurred. If this detection method is adopted into use, it will aid an instructor to carry out the detection process conscientiously.MT 201

Detecção de Suspeita de Plágio de Códigos C para Apoio ao Ensino em Programação

Em turmas de programação, a verificação de práticas de plágio torna mais difícil e demorado o trabalho de um professor. O sistema pPlagio, proposto aqui, auxilia o professor identificando possíveis plágios de códigos em linguagem C de forma automática. O pPlagio faz uso de técnicas para detectar vários tipos de plágios através de processamento prévio dos códigos-fontes e análise de similaridade destes. O sistema é integrado ao Moodle, o que facilita a administração de turmas. O pPlagio foi testado em uma base de códigos utilizada em um evento de competição chamado SOurce COde re-use (SOCO) e os resultados foram melhores que o obtido pelo melhor colocado no evento. Nosso sistema foi superior em 3 situações quando testado e comparado com o Moss

Docker as platform for assignments evaluation

Programming courses are significant part of IT experts' education process. To being able to provide adequate teaching quality in such courses, lecturers should be exempted from routine tasks like source code compilation, testing and grading. Current computers are equipped with enough computational power to automat. These routine tasks. This paper discusse. The analysis and realization of such a system for user submitted automatic source code evaluation. The main system requirement wa. The safe runtime environment (sandbox) for executing potentially dangerous programs. Container based platform Docker was selected after research of ready to use sandbox technologies. This platform simplifies access to isolation mechanism which are implemented i. The current Linux kernel and provides API for system integration. The implemented system around Docker platform is named APAC (Automatic Programming Assignment Checker). I. The paper APAC's architecture and implementation are described and discussed. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article unde. The CC BY-NC-ND license

Digital writing technologies in higher education : theory, research, and practice

This open access book serves as a comprehensive guide to digital writing technology, featuring contributions from over 20 renowned researchers from various disciplines around the world. The book is designed to provide a state-of-the-art synthesis of the developments in digital writing in higher education, making it an essential resource for anyone interested in this rapidly evolving field. In the first part of the book, the authors offer an overview of the impact that digitalization has had on writing, covering more than 25 key technological innovations and their implications for writing practices and pedagogical uses. Drawing on these chapters, the second part of the book explores the theoretical underpinnings of digital writing technology such as writing and learning, writing quality, formulation support, writing and thinking, and writing processes. The authors provide insightful analysis on the impact of these developments and offer valuable insights into the future of writing. Overall, this book provides a cohesive and consistent theoretical view of the new realities of digital writing, complementing existing literature on the digitalization of writing. It is an essential resource for scholars, educators, and practitioners interested in the intersection of technology and writing

Academic integrity : a call to research and action

Originally published in French:L'urgence de l'intégrité académique, Éditions EMS, Management & société, Caen, 2021 (ISBN 978-2-37687-472-0).The urgency of doing complements the urgency of knowing. Urgency here is not the inconsequential injunction of irrational immediacy. It arises in various contexts for good reasons, when there is a threat to the human existence and harms to others. Today, our knowledge based civilization is at risk both by new production models of knowledge and by the shamelessness of knowledge delinquents, exposing the greatest number to important risks. Swiftly, the editors respond to the diagnostic by setting up a reference tool for academic integrity. Across multiple dialogues between the twenty-five chapters and five major themes, the ethical response shapes pragmatic horizons for action, on a range of disciplinary competencies: from science to international diplomacy. An interdisciplinary work indispensable for teachers, students and university researchers and administrators