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- 首页 >> C/C++编程 Security, Privacy and Ethics Coursework 2
Learning outcomes assessed:
B: Evaluating the potential risks and benefits of AI technologies on privacy and personal data
C: Understanding the importance of fairness in AI systems and its implications
Overview
Artificial intelligence has great effect on modern lives. In this coursework, the theme on framework design of new medical image-based bias-mitigated and fair computer-aided diagnosis system will be investigated and explored. The coursework consists of two parts. In Part 1, you need to complete a report based given theme. In Part 2, you need to explain your design via video presentation.
Part 1 (Individual Report: 70 marks)
Healthcare industry is rich of electronic (digital) medical data from different modalities. Deep learning has revolutionized the use of machine learning in healthcare industry by leveraging on the model’s automatic feature extraction and learning. To date, deep models have been applied for numerous computer-aided diagnosis tasks such as prediction, detection and classification. Despite its promising outlook, deep learning- based computer-aided diagnosis models still fail to earn the trust of medical doctors. In fact, there are reports of inaccurate missed diagnoses due to bias error, lack of understanding about the underlying mechanism of deep learning, and miscalibration in real practice. Therefore, there is an open call for fair and transparent computer-aided diagnosis model for trustworthy smart healthcare.
The aim of this task is to empirically assess the current status of computer-aided diagnosis, technologies, challenges and solutions in smart healthcare, and research questions in AI fairness to design a towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design with predefined traits. Hence, you are required to equip yourselves with the understanding about the specific domain of medical imaging. Besides, you need to apply the
knowledge acquired from the lectures and tutorials to complete this coursework. You also need to do literature review to identify further relevant information that is helpful to develop your report content.
Task Instructions:
(1) You are required to study the medical image-based artificial intelligence computer-aided diagnosis by using deep learning in smart healthcare domain. Therefore, literature review is needed. For beginner, you can refer to the suggested review paper to understand the domain of smart healthcare using AI:
Most Nilufa Yeasmin, Md Al Amin, Tasmim Jamal Jati, Zeyar Aung & Mohammad Abdul Azim. 2024. Advanced of AI in Image-Based Computer-Aided Diagnosis: A Review. Array. 23(2024) 100357. Available Online: https://doi.org/10.1016/j.array.2024.10035
Moreover, you are required to study extra learning materials to familiarize yourselves with image- based computer-aided diagnosis by using deep learning. Please note that no mark will be given to the literature review nor content extract from the given review paper. However, this effort shall serve as your first steep for your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.
(2) Write a report on your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design. The report should be written in a clear and concise manner with no more than 1,500 words+/-5%. in total length. Your final report should be detailed, relevant and rationale in addressing the following sections:
Section
Description
Approximate word count
Motivation
You are required to select your project domain from one of the following areas of interest:
• COVID-19 detection
• Thorax abnormality detection
• Late age-related macular degeneration detection
You are required to explain the motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest.
150
Case Study 1
Privacy and ethical challenges associated with large-scale 450 medical image datasets for Artificial Intelligence-based computer-aided diagnosis models
You are required to identify an open access large-scale dataset in your selected area of interest and study the dataset details. Then, you are required to:
• Explain the relevant ethical issues associated with medical image acquisition and sharing for open development of computer-aided diagnosis
• Evaluate the relevant privacy risks associated with medical image data sharing of your selected area of interest in accordance to the Health Insurance Portability and Accountability Act (HIPAA)
• Describe the innovative and rationale data governance measures to achieve better transparency, communication security and sensitive digital medical data protection of your selected area of interest.
Case Study 2 Bias and Fairness in Artificial Intelligence-based computer-aided 450 diagnosis models
You are required to perform analytics approach on existing computer-aided models to help address this case study. Please include necessary evidence of analysis such as detection results, graphs and plots from the python programming codes to support your justification. Please note that no mark will be given to the evidence of analytics approach but it is compulsory to demonstrate your effort in this coursework.
You are required to design a new computer-aided diagnosis model associated with your selected area of interest. The new model should critically cover these traits:
• Innovative bias-mitigation strategy in new AI-based computer-aided diagnosis model’s algorithm setting
• Innovative fairness practice in new AI-based computer-
aided diagnosis model’s social equality setting
• Incorporation of innovative transparency approach in your computer-aided diagnosis model’s algorithm and
cohort criterion.
Framework Design
You are required to propose and explained a new design framework based on the motivation, case study 1 and 2. The new design framework should be illustrated in a detailed overall model diagram. The design framework should encompass the following characteristics:
• Selection of most appropriate AI backbone models for maximum bias-mitigation compared to other state-of- art
• Use of relevant explainable AI for transparent algorithmic mechanism
• Use of relevant evaluation metrics to evaluate the AI fairness improvement
450
Important: Do not repeat existing information that is in the research papers. This will only contribute to low mark. Instead, you need to synthesize your own ideas/opinions based on your understanding and present them in your own words.
Part 2 (Individual Presentation: 30 marks) Task Instructions:
(1) Prepare and record a short individual presentation video of 5 minutes+/-5%. Your presentation should be clear, should be in no more than 10 Powerpoint slides and should not take beyond 5 minutes+/-5%. The presentation should address the followings:
i. To introduce and explain the significance of your proposed towards innovative bias-
mitigated and fair deep learning medical image-based computer-aided
diagnosis model framework design.
ii. To explain how your proposed model design can effectively become General Data
Protection Regulation (GDPR) and IEEE “Human Standards” with Implications for AI compliance in order to promote your design to overseas healthcare market successfully.
Report Format:
Cover Page: This should include the Assessment Number, Assessment Title, Student Name, Student ID and Student Email
Body of the report: This should include all the relevant section headings to address each section as indicated above and marking rubrics.
References: Both your in-text and the references included in the “References” section at the end of the report should adhere strictly to the IEEE reference style.
Formatting requirement:
• Use multiple spacing: 1.08 and spacing after: 8pt;
• Use a standard 12-point font, font type: Tahoma
• Use “Justify” body text
• Put your page numbers at the top right (except the cover page)
• Most importantly, always run a spelling and grammar check; however, remember, such checks
may not pick up all errors. You should still edit your work manually and carefully.
Referencing:
It is compulsory to use IEEE reference style for citing and referencing research. Reference list is excluded from the imposed word limit.
Presentation Format:
Students are not requested to submit their presentation slides to the submission system. However, they must present their Powerpoint presentation slides clearly throughout the video presentation period. Otherwise, the presentation will not be evaluated and ZERO marks will be given.
All video presentation must be uploaded to the Mediasite and attach the video presentation link at the last page of report. It is student responsibility to attach the link properly and apply the right accessibility setting
in Mediasite to ensure examiners can access to the video presentation link in their computers during marking.
Please note that during marking, lecturers are not responsible for any inaccessible video presentation link at their computers due to any kind of reason or under any kind of circumstances, and has the right to give ZERO mark for the inaccessible video presentation link attached in the report.
Marking Criteria
Part 1: Individual Report (70 Marks)
The following criteria will be used to assess the assignment. This report is marked for the whole group.
Outstanding: Report format is well-organized throughout including heading styles, fonts, and margins, figure/table/diagram are critically interpreted and discussed, writing flows extremely fluently from one own idea to another, information is critically deciphered and elaborated in convincing and perfect way with impactful evidence that reflects highly insightful and unique own idea expression, all information is located in the appropriate section. Reference style must be precisely correct and citation arranged sequentially in main text.
Very Good: Report format is properly organized throughout including heading styles, fonts, and margins, figure/table/diagram are effectively interpreted and discussed, writing flows smoothly from one idea to another, information is well explained in a logical, rationale and systematic way with comprehensive evidence that reflects insightful own idea expression, all information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.
Appropriate: Report format is organized and proper, figure/table/diagram are properly interpreted and discussed, sentences are well-structured and words are chosen to communicate ideas clearly that reflects correct own idea expression, information is presented in logical, rationale and systematic way with substantial evidence, information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.
Needs Improvement: Report format is inconsistent and hard to read, figure/table/diagram are poorly interpreted and discussed, sentence structure and/or word choice sometimes interfere with clarity in explaining own idea, information is hard to follow as there is very little continuity, and many items are in the wrong section. Reference style must be correct.
Hard to Understand: Report format is inconsistent and hard to read, figure/table/diagram are not used effectively, sentence structure and word choice make reading and understanding difficult, sequence of information is difficult to follow without much own idea expression, lack of appropriate sections and many items are in the wrong section. Reference style might be incorrect.
No Submission or Missing Section: No submission or missing section of the discussion in the report.
Section Marking criteria Total Marks
Motivation
• The motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest is precisely described.
Marks
• Outstanding: 9 - 10
• VeryGood:7-8
• Appropriate: 5 - 6
• Needs improvement: 3 - 4
• Hard to understand: 1 - 2
• No submission or missing section: 0
/10
Case Study 1
Marks
in the Task Instruction.
• Details associated with Case Study 1 are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Case Study 2
Marks
in the Task Instruction.
• Details associated with Case Study 2 are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Framework Design
Marks
in the Task Instruction.
• Details associated with Framework Design are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Total marks /70
Part 2: Individual Presentation (30 Marks)
You are required to submit a video presentation of 5 minutes+/-5% to the Mediasite and attach the
accessible presentation link. The content of video presentation should reflect the following specifications:
1. Significance of your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.
2. How your proposed model design can become General Data Protection Regulation (GDPR) and Global Initiative on Ethics of Autonomous and Intelligence System compliance
3. How your compliance model can be promoted successfully at overseas healthcare market.
The overall video presentation rubrics are provided below.
Marks 5 4 3 2 1
Significance of proposed framework design
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Model compliance strategy
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Promotion strategy
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Content organization
Content is impactfully- organized and fluent for audience to follow, Use of native-level English without any error, and time control is perfect.
Content is systematically- organised and smooth, Use of fluent English with minimal error pronunciation, time control is superb.
Content is well- organized and acceptable, Use normal English with some unclear pronunciation, time control is properly- managed.
Content is average level and somehow hard to follow, Use of poor English with unclear pronunciation, time control is poor.
Content is messy and very hard to follow, Use of poor English with not understandable pronunciation, time control is unacceptable.
Presentation Delivery mode Delivery mode Delivery mode Delivery mode Delivery mode skills is precisely is highly is confident, lacks is in disarray poised, confident, flow flow is properly confidence and without any
charmingly confident, flow is accurately controlled and fluent, interaction is creative, skillful and attractive.
is well- controlled and smooth, interaction is well managed, skillful and attractive.
managed and smooth, interaction is properly managed and average.
smoothness, flow is mediocrely managed, interaction is boring.
sense of confident, flow is poorly managed and interaction is very boring.
Learning outcomes assessed:
B: Evaluating the potential risks and benefits of AI technologies on privacy and personal data
C: Understanding the importance of fairness in AI systems and its implications
Overview
Artificial intelligence has great effect on modern lives. In this coursework, the theme on framework design of new medical image-based bias-mitigated and fair computer-aided diagnosis system will be investigated and explored. The coursework consists of two parts. In Part 1, you need to complete a report based given theme. In Part 2, you need to explain your design via video presentation.
Part 1 (Individual Report: 70 marks)
Healthcare industry is rich of electronic (digital) medical data from different modalities. Deep learning has revolutionized the use of machine learning in healthcare industry by leveraging on the model’s automatic feature extraction and learning. To date, deep models have been applied for numerous computer-aided diagnosis tasks such as prediction, detection and classification. Despite its promising outlook, deep learning- based computer-aided diagnosis models still fail to earn the trust of medical doctors. In fact, there are reports of inaccurate missed diagnoses due to bias error, lack of understanding about the underlying mechanism of deep learning, and miscalibration in real practice. Therefore, there is an open call for fair and transparent computer-aided diagnosis model for trustworthy smart healthcare.
The aim of this task is to empirically assess the current status of computer-aided diagnosis, technologies, challenges and solutions in smart healthcare, and research questions in AI fairness to design a towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design with predefined traits. Hence, you are required to equip yourselves with the understanding about the specific domain of medical imaging. Besides, you need to apply the
knowledge acquired from the lectures and tutorials to complete this coursework. You also need to do literature review to identify further relevant information that is helpful to develop your report content.
Task Instructions:
(1) You are required to study the medical image-based artificial intelligence computer-aided diagnosis by using deep learning in smart healthcare domain. Therefore, literature review is needed. For beginner, you can refer to the suggested review paper to understand the domain of smart healthcare using AI:
Most Nilufa Yeasmin, Md Al Amin, Tasmim Jamal Jati, Zeyar Aung & Mohammad Abdul Azim. 2024. Advanced of AI in Image-Based Computer-Aided Diagnosis: A Review. Array. 23(2024) 100357. Available Online: https://doi.org/10.1016/j.array.2024.10035
Moreover, you are required to study extra learning materials to familiarize yourselves with image- based computer-aided diagnosis by using deep learning. Please note that no mark will be given to the literature review nor content extract from the given review paper. However, this effort shall serve as your first steep for your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.
(2) Write a report on your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design. The report should be written in a clear and concise manner with no more than 1,500 words+/-5%. in total length. Your final report should be detailed, relevant and rationale in addressing the following sections:
Section
Description
Approximate word count
Motivation
You are required to select your project domain from one of the following areas of interest:
• COVID-19 detection
• Thorax abnormality detection
• Late age-related macular degeneration detection
You are required to explain the motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest.
150
Case Study 1
Privacy and ethical challenges associated with large-scale 450 medical image datasets for Artificial Intelligence-based computer-aided diagnosis models
You are required to identify an open access large-scale dataset in your selected area of interest and study the dataset details. Then, you are required to:
• Explain the relevant ethical issues associated with medical image acquisition and sharing for open development of computer-aided diagnosis
• Evaluate the relevant privacy risks associated with medical image data sharing of your selected area of interest in accordance to the Health Insurance Portability and Accountability Act (HIPAA)
• Describe the innovative and rationale data governance measures to achieve better transparency, communication security and sensitive digital medical data protection of your selected area of interest.
Case Study 2 Bias and Fairness in Artificial Intelligence-based computer-aided 450 diagnosis models
You are required to perform analytics approach on existing computer-aided models to help address this case study. Please include necessary evidence of analysis such as detection results, graphs and plots from the python programming codes to support your justification. Please note that no mark will be given to the evidence of analytics approach but it is compulsory to demonstrate your effort in this coursework.
You are required to design a new computer-aided diagnosis model associated with your selected area of interest. The new model should critically cover these traits:
• Innovative bias-mitigation strategy in new AI-based computer-aided diagnosis model’s algorithm setting
• Innovative fairness practice in new AI-based computer-
aided diagnosis model’s social equality setting
• Incorporation of innovative transparency approach in your computer-aided diagnosis model’s algorithm and
cohort criterion.
Framework Design
You are required to propose and explained a new design framework based on the motivation, case study 1 and 2. The new design framework should be illustrated in a detailed overall model diagram. The design framework should encompass the following characteristics:
• Selection of most appropriate AI backbone models for maximum bias-mitigation compared to other state-of- art
• Use of relevant explainable AI for transparent algorithmic mechanism
• Use of relevant evaluation metrics to evaluate the AI fairness improvement
450
Important: Do not repeat existing information that is in the research papers. This will only contribute to low mark. Instead, you need to synthesize your own ideas/opinions based on your understanding and present them in your own words.
Part 2 (Individual Presentation: 30 marks) Task Instructions:
(1) Prepare and record a short individual presentation video of 5 minutes+/-5%. Your presentation should be clear, should be in no more than 10 Powerpoint slides and should not take beyond 5 minutes+/-5%. The presentation should address the followings:
i. To introduce and explain the significance of your proposed towards innovative bias-
mitigated and fair deep learning medical image-based computer-aided
diagnosis model framework design.
ii. To explain how your proposed model design can effectively become General Data
Protection Regulation (GDPR) and IEEE “Human Standards” with Implications for AI compliance in order to promote your design to overseas healthcare market successfully.
Report Format:
Cover Page: This should include the Assessment Number, Assessment Title, Student Name, Student ID and Student Email
Body of the report: This should include all the relevant section headings to address each section as indicated above and marking rubrics.
References: Both your in-text and the references included in the “References” section at the end of the report should adhere strictly to the IEEE reference style.
Formatting requirement:
• Use multiple spacing: 1.08 and spacing after: 8pt;
• Use a standard 12-point font, font type: Tahoma
• Use “Justify” body text
• Put your page numbers at the top right (except the cover page)
• Most importantly, always run a spelling and grammar check; however, remember, such checks
may not pick up all errors. You should still edit your work manually and carefully.
Referencing:
It is compulsory to use IEEE reference style for citing and referencing research. Reference list is excluded from the imposed word limit.
Presentation Format:
Students are not requested to submit their presentation slides to the submission system. However, they must present their Powerpoint presentation slides clearly throughout the video presentation period. Otherwise, the presentation will not be evaluated and ZERO marks will be given.
All video presentation must be uploaded to the Mediasite and attach the video presentation link at the last page of report. It is student responsibility to attach the link properly and apply the right accessibility setting
in Mediasite to ensure examiners can access to the video presentation link in their computers during marking.
Please note that during marking, lecturers are not responsible for any inaccessible video presentation link at their computers due to any kind of reason or under any kind of circumstances, and has the right to give ZERO mark for the inaccessible video presentation link attached in the report.
Marking Criteria
Part 1: Individual Report (70 Marks)
The following criteria will be used to assess the assignment. This report is marked for the whole group.
Outstanding: Report format is well-organized throughout including heading styles, fonts, and margins, figure/table/diagram are critically interpreted and discussed, writing flows extremely fluently from one own idea to another, information is critically deciphered and elaborated in convincing and perfect way with impactful evidence that reflects highly insightful and unique own idea expression, all information is located in the appropriate section. Reference style must be precisely correct and citation arranged sequentially in main text.
Very Good: Report format is properly organized throughout including heading styles, fonts, and margins, figure/table/diagram are effectively interpreted and discussed, writing flows smoothly from one idea to another, information is well explained in a logical, rationale and systematic way with comprehensive evidence that reflects insightful own idea expression, all information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.
Appropriate: Report format is organized and proper, figure/table/diagram are properly interpreted and discussed, sentences are well-structured and words are chosen to communicate ideas clearly that reflects correct own idea expression, information is presented in logical, rationale and systematic way with substantial evidence, information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.
Needs Improvement: Report format is inconsistent and hard to read, figure/table/diagram are poorly interpreted and discussed, sentence structure and/or word choice sometimes interfere with clarity in explaining own idea, information is hard to follow as there is very little continuity, and many items are in the wrong section. Reference style must be correct.
Hard to Understand: Report format is inconsistent and hard to read, figure/table/diagram are not used effectively, sentence structure and word choice make reading and understanding difficult, sequence of information is difficult to follow without much own idea expression, lack of appropriate sections and many items are in the wrong section. Reference style might be incorrect.
No Submission or Missing Section: No submission or missing section of the discussion in the report.
Section Marking criteria Total Marks
Motivation
• The motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest is precisely described.
Marks
• Outstanding: 9 - 10
• VeryGood:7-8
• Appropriate: 5 - 6
• Needs improvement: 3 - 4
• Hard to understand: 1 - 2
• No submission or missing section: 0
/10
Case Study 1
Marks
in the Task Instruction.
• Details associated with Case Study 1 are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Case Study 2
Marks
in the Task Instruction.
• Details associated with Case Study 2 are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Framework Design
Marks
in the Task Instruction.
• Details associated with Framework Design are precisely explained to fulfil the task description as per highlighted
• Outstanding: 16 - 20
• Very Good: 13 - 16
• Appropriate: 9 - 12
• Needs improvement: 5 - 8
• Hard to understand: 1 – 4
• No submission or missing section: 0
/20
Total marks /70
Part 2: Individual Presentation (30 Marks)
You are required to submit a video presentation of 5 minutes+/-5% to the Mediasite and attach the
accessible presentation link. The content of video presentation should reflect the following specifications:
1. Significance of your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.
2. How your proposed model design can become General Data Protection Regulation (GDPR) and Global Initiative on Ethics of Autonomous and Intelligence System compliance
3. How your compliance model can be promoted successfully at overseas healthcare market.
The overall video presentation rubrics are provided below.
Marks 5 4 3 2 1
Significance of proposed framework design
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Model compliance strategy
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Promotion strategy
Content is unique, impactful and insightful, and outperform the specification
Content is concise, informative, factful and relevant, and exceed the specification
Content is comprehensive, logical and relevant, and meet the specification
Content is good, relevant and meet specification but lacks informativeness
Content is irrelevant and does not meet specification
Content organization
Content is impactfully- organized and fluent for audience to follow, Use of native-level English without any error, and time control is perfect.
Content is systematically- organised and smooth, Use of fluent English with minimal error pronunciation, time control is superb.
Content is well- organized and acceptable, Use normal English with some unclear pronunciation, time control is properly- managed.
Content is average level and somehow hard to follow, Use of poor English with unclear pronunciation, time control is poor.
Content is messy and very hard to follow, Use of poor English with not understandable pronunciation, time control is unacceptable.
Presentation Delivery mode Delivery mode Delivery mode Delivery mode Delivery mode skills is precisely is highly is confident, lacks is in disarray poised, confident, flow flow is properly confidence and without any
charmingly confident, flow is accurately controlled and fluent, interaction is creative, skillful and attractive.
is well- controlled and smooth, interaction is well managed, skillful and attractive.
managed and smooth, interaction is properly managed and average.
smoothness, flow is mediocrely managed, interaction is boring.
sense of confident, flow is poorly managed and interaction is very boring.