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CSCI814 IT Project Management
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CSCI814 IT Project Management
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Course Code: CSCI814
University: University Of Wollongong
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Country: Australia
Question:
(1) Progress report is due in week 4 lab, where a two-page hardcopy progress report and two-minutes oral presentation are to be given to the tutor. The progress report should include the current progress and future project plan.
(2) A softcopy (preferably an MS PowerPoint file or a PDF file generated from the PowerPoint file) of the group’s final project presentation must be submitted to Moodle (deadline: 11:30 am, Tuesday 23 Oct 2018).
(3) A hardcopy of the group’s final project report and presentation slides, together with their softcopies, program code, and supplementary data (in a USB or disk storage), must be submitted to the lecturer in person at the start of week 13 lecture.
(4) Each group must give a final project presentation in week 13 lecture. All group members must stand in front of the class during their presentation, although we do not require every student to speak.
On the cover page of your report, indicate the contribution of each team member, and everyone in the team should sign. Different team members may receive different marks based on their individual contribution. The “individual contribution” of each team member is assessed by all the other members (the scale is: “contributed”, “very little”, and “almost no contribution”). For a team member who has “contributed”, he/she will receive 100% of the group mark; for a team member who contributed “very little”, he/she will receive 50% of the team mark; for students who made “almost no contribution”, he/she will receive 0 marks for the entire group project. Your tutor may make adjustment to this marking criterion based on practical situations.
Project description:
In this assignment, a team of 5 to 7 students will create a cross-language plagiarism detection tool. As a minimum requirement, the system should provide the following functions:
User management (keep track of users, who log in using username/password).
Document management (store and keep track of documents and link them to users).
Allow the user to provide a plain text file containing an article written in English.
Allow the user to select at least one target language other than English.
The tool will search the Internet for similar articles writing in English or in the target language.
Report the degree of similarity in terms of percentage.
Highlight the similarities between the two files.
The system can be developed in any programming language and database that are available in your lab, so that you can demonstrate your system to your tutor in the lab. Furthermore, you are allowed to use any existing software components including, for example, open source software and web services/APIs such as Microsoft Translator Text API and Yandex Translate API. However, you must explicitly declare whichpart is your own work and which part is taken from other people’s work (declare at the very start of your final report), or else it is plagiarism.
In addition to the final report file and final presentation file, the submitted external storage should contain the source code (with at least 1/3 comments) and executable code of the software you developed, screenshots, test data and results, and any other documents you wish to include such as a readme file. Marking criteria:
Progress report: 1 mark
Final project presentation: 1 mark
An intensive literature review and analysis of related technologies, tools and services. 5 marks
Project management (project and activity planning, progress and controlling, effort estimation, risk analysis and measures, meeting records, degree of success of the project, etc). 7 marks
Note (1): You must include a project charter and a project diary. A project charter should include at least the project’s title and date of authorization, the project manager’s information (group representative or all team members), a summary schedule, a summary of budget (if applicable), project objectives, project success criteria, a summary of the planned approach for managing the project, roles and responsibilities. In real-life projects, it should be reviewed by all project stakeholders—therefore, you should include a list of stakeholders.
Note (2): You can use project management tools such as Microsoft Project, but it is not compulsory.
Note (3): Tutors may assess your project management quality not only based on your report but also based on their weekly observations of your progress in the lab.
Requirements analysis and specification, and software design. 6 marks
Implementation (quality of code, fulfilment of functional requirements, soundness of solution, consistency with design, etc). 10 marks
Note: You may include additional features (upon user agreement) if the basic functional requirements are fulfilled.
Testing (test plan, test cases, test executions and test results): 5 marks
Usability and user interface: 5 marks
FAQ 1: What is the format of the project report?
Answer: You decide. Your report needs to address the above marking criteria.
FAQ 2: How many test cases should we design?
Answer: You decide, and justify your decision in your report. You should adopt a black-box or whitebox test adequacy criterion.
FAQ 3: What should we include in the softcopy of the final submission?
Answer: Anything that you consider necessary to include. At least, the following should be included:
your source code, executable code, test cases and test results, and a readme file showing the screenshots of successful executions of your tool. You need to provide evidence to show that your tool can execute successfully. An example of such evidence is that, once you collected the outputs, you manually verify each output and then report whether or not they are correct.
FAQ 4: The functional requirements given in this document are a bit vague …
Answer: This is to make your project more like a real-world one where the users might not be very clear about what they wanted at the very start of the project. Each lab provides you with an opportunity to interview your user (tutor) to understand what they really want. You are encouraged to come to your user with a prototype interface so that your discussions will be more concrete.
Answer:
Introduction
This application aims to take in an input from a user which would be a text file. The two languages that can be read from the file should be English and Hindi. The users will be able to provide the input as an UNICODE file. To achieve this we create a “Hindi representation” of the sentence in English.The application will then search for similar files on the internet and provide as with the results that are relevant to the text file that is uploaded. To achieve this we create a “Hindi representation” of the sentence in English.
Literature Review
We went through many articles on the internet’s which were related to the development of the cross platform plagiarism tool. An article [1] on the stack overflow suggested that we could develop this application in Python using the NLTK library and GenSem library which is accomplished by creating the LDA or LSA of the document. We can ultimately use the Google Search API to search for those words. NTLK [2] is the Natural Language Toolkit for the natural language processing. This toolkit supports libraries for classification, tokenization, stemming, tagging, parsing, semantic reasoning etc.
In [5], Chowet. al. mentions about the semantic plagiarism technique. Semantic plagiarism is where the sentence is reconstructed or some terms are changed into its corresponding synonyms. Both of these plagiarisms is hardly detected due to the difference in their fingerprints. Plagiarism detection tools that are available are not capable to detect such plagiarism cases.
Chow et. al. in [5] proposes a new approach in detecting both cross language and semantic plagiarism, where , the query document is shortened by utilising fuzzy swarm-based summarisation approach, the summary will give the most important keywords in the document. Input summary documents are translated into English using Google Translate Application Programming Interface (API) before the words are stemmed and the stop words are removed. Tokenized documents are sent to the Google AJAX Search API to search for similar documents throughout the World Wide Web. Stanford Parser and Word Net are used to determine the semantic similarity between the suspected documents with source documents. Stanford parser assigns each terms in the sentence to their corresponding roles such as Nouns, Verbs and Adjectives. Each sentence is then represented in a predicate form and similarity is measured based on those predicates using information from Word Net taxonomy. Testing dataset is built up from two sets of input documents which are produced based on different plagiarism techniques.
Bird et. al. in [3] overs the scope of using the NTLK toolkit for the natural language processing. We are thinking of using methodology where a Token class is used to represent of unit a text such as a word, sentence or a piece of document. Kuhn et. al.[4] describes the use of the application of semantic classification trees for the understanding of natural language processing. Speech understanding, semantic classification, machine learning, natural language and decision tree based capabilities for a translator application are covered up in this paper.
These paragraphs speakabout the speech classification, machine learning based learning of artificial neural networks, decision trees, tokenization and several other methods.In [6], Jeremy et. al. talks about different state-of-art methods to detect the plagiarism. Some of the methods used in the experiment are Cross-Language Character N-Gram (CL-CnG) , Cross-Language Conceptual Thesaurus-based Similarity (CL-CTS), Cross-Language Alignment-based Similarity Analysis (CL-ASA), Cross-Language Explicit Semantic Analysis (CL-ESA), Translation + Monolingual Analysis (T+MA). According to the author, there is a common behaviour of each method across different language pairs. There is not only a strong correlation across languages but also across text units that were considered. If a method is efficient on a particular language pair, it will be similarly efficient on another language pair as long as enough lexical resources are available for these languages. There was a strong correlation across types of text when they investigated the behaviour of the methods across different types of texts on a particular language pair. It was found that a method could be optimized on a particular collection of text and applied efficiently on another collection. Finally, it was concluded that methods behave differently in clustering match and mismatched units, even if they seem similar in performance.
Project Management
The Project Activities are shown below(Barrón-Cedeño, Gupta and Rosso, 2013).
Developing a cross-language plagiarism detection tool
User management
Document management
Translation of input documents
Translate the plagiarized Hindi documents into English
Improve the effectiveness of the detection process
Use Google Translate AP
Removing Stop Words
Before passing the translated documents for comparison through the Internet
Remove the stop words in the translated text
Stemming Words
Remove the affixes
Generate root word
Pattern matching
Text Stemmer and Porter Stemmer
Use of Porter Stemming algorithm
Removing the commoner morphological and in flexional endings from words in English
Identifying Similar Documents
Collection of documents that located around the World Wide Web
Enables small and characteristic fragments translation
Query documents or texts are inserted
Use of Google AJAX Search API
Comparison of Similar Pattern
Detect plagiarism
Represent the sentence uniquely.
Summary of the Result
Gathering the result
Plagiarism detection is displayed
Highlight the similarities between the two files.
Resources are shown below.
Resource Name
Type
Initials
Max. Units
Std. Rate
Accrue At
Base Calendar
Project Manager
Work
P
100%
$1,000.00/hr
Prorated
Standard
System Analyst
Work
S
100%
$1,000.00/hr
Prorated
Standard
Developer
Work
D
100%
$1,000.00/hr
Prorated
Standard
Designer
Work
D
100%
$1,000.00/hr
Prorated
Standard
Technical Writer
Work
T
100%
$1,000.00/hr
Prorated
Standard
Code Designer
Work
C
100%
$1,000.00/hr
Prorated
Standard
Overall Project Activities are shown below(Chauhan, Arora and Singhal, 2017).
Task Name
Duration
Start
Finish
Predecessors
Resource Names
Developing a cross-language plagiarism detection tool
60 days
Wed 9/12/18
Tue 12/4/18
User management
1 day
Wed 9/12/18
Wed 9/12/18
Designer, Developer
Document management
2 days
Thu 9/13/18
Fri 9/14/18
2
Designer, Project Manager, Technical Writer
Translation of input documents
8 days
Mon 9/17/18
Wed 9/26/18
3
Translate the plagiarized Hindi documents into English
2 days
Mon 9/17/18
Tue 9/18/18
Code Designer, Developer, System Analyst
Improve the effectiveness of the detection process
3 days
Wed 9/19/18
Fri 9/21/18
5
Developer
Use Google Translate AP
3 days
Mon 9/24/18
Wed 9/26/18
6
Code Designer, Designer
Removing Stop Words
5 days
Thu 9/27/18
Wed 10/3/18
4
Before passing the translated documents for comparison through the Internet
2 days
Thu 9/27/18
Fri 9/28/18
Designer, System Analyst
Remove the stop words in the translated text
3 days
Mon 10/1/18
Wed 10/3/18
9
Developer, Code Designer
Stemming Words
15 days
Thu 10/4/18
Wed 10/24/18
8
Remove the affixes
3 days
Thu 10/4/18
Mon 10/8/18
Designer
Generate root word
4 days
Tue 10/9/18
Fri 10/12/18
12
System Analyst
Pattern matching
2 days
Mon 10/15/18
Tue 10/16/18
13
Designer
Text Stemmer and Porter Stemmer
2 days
Wed 10/17/18
Thu 10/18/18
14
Developer
Use of Porter Stemming algorithm
2 days
Fri 10/19/18
Mon 10/22/18
15
Developer
Removing the commoner morphological and in flexional endings from words in English
2 days
Tue 10/23/18
Wed 10/24/18
16
Developer, System Analyst
Identifying Similar Documents
10 days
Thu 10/25/18
Wed 11/7/18
11
Collection of documents that located around the World Wide Web
2 days
Thu 10/25/18
Fri 10/26/18
System Analyst
Enables small and characteristic fragments translation
3 days
Thu 10/25/18
Mon 10/29/18
Developer
Query documents or texts are inserted
3 days
Tue 10/30/18
Thu 11/1/18
20
System Analyst, Technical Writer
Use of Google AJAX Search API
4 days
Fri 11/2/18
Wed 11/7/18
21
Code Designer, Developer
Comparison of Similar Pattern
10 days
Thu 11/8/18
Wed 11/21/18
18
Detect plagiarism
4 days
Tue 11/13/18
Fri 11/16/18
24
Code Designer, Project Manager, System Analyst
Represent the sentence uniquely.
3 days
Mon 11/19/18
Wed 11/21/18
25
System Analyst, Technical Writer
Summary of the Result
9 days
Thu 11/22/18
Tue 12/4/18
23
Gathering the result
2 days
Thu 11/22/18
Fri 11/23/18
Project Manager, System Analyst
Plagiarism detection is displayed
3 days
Mon 11/26/18
Wed 11/28/18
28
Designer, Developer, Project Manager
Highlight the similarities between the two files.
4 days
Thu 11/29/18
Tue 12/4/18
29
Code Designer, Developer
Project charter is shown below.
Resource Cost status is shown below(Ehsan and Shakery, 2016).
Project Activities Cost is shown below.
Name
Fixed Cost
Actual Cost
Remaining Cost
Cost
Baseline Cost
Cost Variance
Developing a cross-language plagiarism detection tool
$0.00
$0.00
$912,000.00
$912,000.00
$0.00
$912,000.00
MS Project file is attached here.
Requirement analysis and Specification
Plagiarism is turning into a difficult issue for scholarly network. The recognition of counterfeiting at different levels is an important issue. The complexity of the issue increments when we are finding the plagiarism detection in the source codes that might be in a similar language or they have been changed into different languages(Franco-Salvador et al., 2016). This kind of written falsification is found in the scholastic fills in as well as in the ventures managing programming planning. The real issue with the source code written fabrication is that distinctive programming languages may have different linguistic structure.
In view of language homogeneity or heterogeneity of the writings being looked at, plagiarism detection discovery can be characterized into monolingual and cross-lingual. The cross-language written misrepresentation recognition process is like the outside plagiarism detection identification assignment with a few alterations in heuristic recovery and itemized investigation stages(Gelbukh, 2009). In cross-language heuristic recovery, this stage expects to recover the accumulation of source hopeful archives from the informational index. Deciphering the info archive from the inquiry language to the source language might be required in this stage. The cross-language point by point examination level estimates the cross-language likeness between segments of the suspicious record and segments of the hopeful reports which recovered in the past stage(Kashkur, Parshutin and Borisov, 2010).
Language used : Java script.
Software Design
Software Design for Cross language plagiarism detection tool is illustrated below(Kasprowicz and Wada, 2014).
Usability and User Design
If you type the any text it change from English to hindi..(Potthast et al., 2010)
Conclusion
With the project being accomplished, we hope that we would be able to find plagiarism related to any articles on the web provided an input file to our application.
References
‘How to develop a plagiarism detector?’ stackoverflow.com/questions/1193408 extracted on 11 August 2018.
NTLK 3.3 documentation for Natural Language Toolkit extracted from nltk.org on 11 August 2018.
Steven Bird, Edward Loper NTLK: The Natural Language Toolkit
Roland Kuhn, Renato De Mori – The Application of Semantic Classification Trees to Natural Language Understanding.
Chow Kok Kent, NaomieSalim- Web Based Cross Language Semantic Plagiarism Detection, 03 January, 2012
Jeremy Ferrero, Lauren Besacier, Didier Schwab, Frederic Agnes- Deep Investigation of Cross-Language Plagiarism Detection MethodsBarrón-Cedeño, A., Gupta, P. and Rosso, P. (2013). Methods for cross-language plagiarism detection. Knowledge-Based Systems, 50, pp.211-217.
Chauhan, S., Arora, A. and Singhal, Y. (2017). Plagiarism Detection of C Program using Assembly Language. International Journal of Computer Applications, 158(3), pp.17-22.
Ehsan, N. and Shakery, A. (2016). Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information. Information Processing & Management, 52(6), pp.1004-1017.
Franco-Salvador, M., Gupta, P., Rosso, P. and Banchs, R. (2016). Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language. Knowledge-Based Systems, 111, pp.87-99.
Gelbukh, A. (2009). Computational Linguistics and Intelligent Text Processing. Heidelberg: Springer.
Kashkur, M., Parshutin, S. and Borisov, A. (2010). Research into Plagiarism Cases and Plagiarism Detection Methods. Scientific Journal of Riga Technical University. Computer Sciences, 42(1).
Kasprowicz, D. and Wada, H. (2014). Methods for automated detection of plagiarism in integrated-circuit layouts. Microelectronics Journal, 45(9), pp.1212-1219.
Lee, Y. (2012). Plagiarism Detection among Source Codes using Adaptive Methods. KSII Transactions on Internet and Information Systems.
METHODS FOR INTRINSIC PLAGIARISM DETECTION. (2017). Informatics and Applications.
Potthast, M., Barrón-Cedeño, A., Stein, B. and Rosso, P. (2010). Cross-language plagiarism detection. Language Resources and Evaluation, 45(1), pp.45-62.
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