Works (20)

Updated: April 15th, 2024 08:54

2024 conference paper

Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models

Hoq, M., Shi, Y., Leinonen, J., Babalola, D., Lynch, C., Price, T., & Akram, B. (2024, March 7).

TL;DR: This work evaluated the performance of both traditional machine learning models and Abstract Syntax Tree-based (AST-based) deep learning models in detecting ChatGPT code from student code submissions, and suggested that both traditional machine learning models and AST-based deep learning models are effective in identifying ChatGPT-generated code with accuracy above 90%. (via Semantic Scholar)
Source: ORCID
Added: March 8, 2024

2024 conference paper

Enhancing Code Tracing Question Generation with Refined Prompts in Large Language Models

Fan, A. X., Hendrawan, R. A., Shi, Y., & Ma, Q. (2024, March 14).

By: A. Fan*, R. Hendrawan*, Y. Shi n & Q. Ma*

Source: ORCID
Added: March 22, 2024

2024 conference paper

Novices' Perceptions of Web-Search and AI for Programming

Skripchuk, J., Bacher, J., Shi, Y., Tran, K., & Price, T. (2024, March 14).

Source: ORCID
Added: March 22, 2024

2023 article

Investigating the Impact of On-Demand Code Examples on Novices' Open-Ended Programming Projects

PROCEEDINGS OF THE 2023 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH V.1, ICER 2023 V1, pp. 464–475.

By: W. Wang*, J. Bacher n, A. Isvik n, A. Limke n, S. Sthapit n, Y. Shi n, B. Tabarsi n, K. Tran n ...

author keywords: open-ended programming; code examples; block-based programming; novice programming
TL;DR: It was found that students who had access to all 37 code examples used a significantly larger variety of code APIs, perceived the programming as relatively more creative, but also experienced a higher task load. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: September 11, 2023

2022 article

Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks

ArXiv.

By: Y. Shi*, M. Chi, T. Barnes & T. Price

Contributors: Y. Shi*, M. Chi, T. Barnes & T. Price

TL;DR: This work proposes Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT, and shows that Code-D KT consistently outperforms DKT. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: ORCID
Added: December 10, 2022

2022 conference paper

Developing Comic-based Learning Toolkits for Teaching Computing to Elementary School Learners

Castro, F., Suh, S., E, J. L., Naowaprateep, W., & Shi, Y. (2022, March).

TL;DR: The use of comics to teach computing by having learners create, design, and arrange comic panels to support the informal learning of computing concepts for elementary school learners through a physical comic-based learning toolkit. (via Semantic Scholar)
Source: ORCID
Added: March 7, 2023

2022 article

Identifying Common Errors in Open-Ended Machine Learning Projects

PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1, Vol. 1, pp. 216–222.

By: J. Skripchuk n, Y. Shi n & T. Price n

Contributors: J. Skripchuk n, Y. Shi n & T. Price n

author keywords: Computer science education; Machine learning education; Data science
TL;DR: This work qualitatively coded over 2,500 cells of code from 19 final team projects in an upper-division machine learning course to identify what ML errors students struggle with, and found that library usage, hyperparameter tuning, and misusing test data were among the most common errors. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: ORCID, Web Of Science
Added: July 23, 2022

2021 conference paper

Investigate Effectiveness of Code Features in Knowledge Tracing Task on Novice Programming Course

CEUR Workshop Proceedings, 3051. http://www.scopus.com/inward/record.url?eid=2-s2.0-85122896934&partnerID=MN8TOARS

By: P. Penmetsa, Y. Shi & T. Price

Contributors: P. Penmetsa, Y. Shi & T. Price

Source: ORCID
Added: December 10, 2022

2021 conference paper

Just a Few Expert Constraints Can Help: Humanizing Data-Driven Subgoal Detection for Novice Programming

Educational Data Mining 2021. https://eric.ed.gov/?id=ED615599

Yang Shi

Source: ORCID
Added: January 7, 2022

2021 conference paper

Knowing both when and where: Temporal-ASTNN for Early Prediction of Student Success in Novice Programming Tasks

Educational Data Mining 2021. https://eric.ed.gov/?id=ED615543

Yang Shi

Source: ORCID
Added: January 7, 2022

2021 conference paper

More With Less: Exploring How to Use Deep Learning Effectively through Semi-supervised Learning for Automatic Bug Detection in Student Code

Educational Data Mining 2021. https://eric.ed.gov/?id=ED615586

Yang Shi

Source: ORCID
Added: January 7, 2022

2021 article

Toward Semi-Automatic Misconception Discovery Using Code Embeddings

LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, pp. 606–612.

By: Y. Shi n, K. Shah n, W. Wang n, S. Marwan n, P. Penmetsa n & T. Price n

Contributors: Y. Shi n, K. Shah n, W. Wang n, S. Marwan n, P. Penmetsa n & T. Price n

author keywords: Neural Network; Code Analysis; Automatic Assessment; Learning Representation
TL;DR: This work presents a novel method for the semi-automated discovery of problem-specific misconceptions from students’ program code in computing courses, using a state-of-the-art code classification model. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: October 21, 2021

2021 article

Toward semi-automatic misconception discovery using code embeddings

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85102809405&partnerID=MN8TOARS

By: Y. Shi, K. Shah, W. Wang, S. Marwan, P. Penmetsa & T. Price

Contributors: Y. Shi, K. Shah, W. Wang, S. Marwan, P. Penmetsa & T. Price

Source: ORCID
Added: December 10, 2022

2020 conference paper

Comparing feature engineering approaches to predict complex programming behaviors

CEUR Workshop Proceedings, 2734. http://www.scopus.com/inward/record.url?eid=2-s2.0-85096164837&partnerID=MN8TOARS

By: W. Wang, Y. Rao, Y. Shi, A. Milliken, C. Martens, T. Barnes, T. Price

Contributors: W. Wang, Y. Rao, Y. Shi, A. Milliken, C. Martens, T. Barnes, T. Price

Source: ORCID
Added: December 10, 2022

2020 article

Test_positive at W-nut 2020 shared task-3: Joint event multi-task learning for slot filling in noisy text

ArXiv. http://www.scopus.com/inward/record.url?eid=2-s2.0-85098406978&partnerID=MN8TOARS

By: C. Chen, C. Huang, Y. Hou, Y. Shi, E. Dai & J. Wang

Contributors: C. Chen, C. Huang, Y. Hou, Y. Shi, E. Dai & J. Wang

Source: ORCID
Added: December 10, 2022

2019 conference paper

Energy audition based cyber-physical attack detection system in IoT

Proceedings of the ACM Turing Celebration Conference - China.

By: Y. Shi*, F. Li, W. Song, X. Li* & J. Ye

Contributors: Y. Shi*, F. Li, W. Song, X. Li* & J. Ye

TL;DR: The framework applies a data-centric method to process the energy consumption data and classify the attack status of the monitored device, and is more secure in cases the kernel of the device is already compromised. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: October 21, 2021

2019 journal article

Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing

IEEE Internet of Things Journal, 6(3), 5224–5231.

By: F. Li*, Y. Shi*, A. Shinde*, J. Ye* & W. Song*

Contributors: F. Li*, Y. Shi*, A. Shinde*, J. Ye* & W. Song*

author keywords: Cyber and physical attack detection; deep learning (DL); energy audit; Internet of Things (IoT)
TL;DR: This is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing and analytics-based IoT monitoring mechanism using the energy meter readings and proposes a disaggregation-aggregation architecture. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: September 4, 2019

2019 journal article

System Statistics Learning-Based IoT Security: Feasibility and Suitability

IEEE Internet of Things Journal, 6(4), 6396–6403.

By: F. Li*, A. Shinde*, Y. Shi*, J. Ye*, X. Li* & W. Song*

Contributors: F. Li*, A. Shinde*, Y. Shi*, J. Ye*, X. Li* & W. Song*

author keywords: Anomaly detection; deep learning; failure and intrusion detection; Internet of Things (IoT); machine learning
TL;DR: It is concluded that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred. (via Semantic Scholar)
Source: ORCID
Added: September 4, 2019

2018 conference paper

Dynamic Time-frequency Feature Extraction for Brain Activity Recognition

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018-July, 3104–3107.

By: Y. Shi*, F. Li*, T. Liu*, F. Beyette* & W. Song*

Contributors: Y. Shi*, F. Li*, T. Liu*, F. Beyette* & W. Song*

MeSH headings : Algorithms; Brain; Electroencephalography; Imagery, Psychotherapy; Signal Processing, Computer-Assisted
TL;DR: This paper proposes an improved dynamic feature extraction approach based on a time-frequency representation and an optimal sequence similarity measurement that demonstrates the importance of the advanced feature extraction in time series data analysis, e.g. biomedical signal. (via Semantic Scholar)
Source: ORCID
Added: September 4, 2019

2018 article

PoTrojan: powerful neural-level trojan designs in deep learning models

(2018, February 8).

Yang Shi

Source: ORCID
Added: September 4, 2019

Employment

Updated: January 5th, 2022 12:48

2019 - present

North Carolina State University Raleigh, North Carolina, US
Graduate Assistant Department of Computer Science

2019 - 2019

Stratifyd Inc Charlotte, NC, US
Research Scientist Intern

2015 - 2019

University of Georgia Athens, GA, US
Graduate Assistant Computer Science

Education

Updated: January 5th, 2022 12:49

2019 - present

North Carolina State University Raleigh, NC, US
Doctor of Philosophy Department of Computer Science

2018 - 2019

University of Georgia Athens, GA, US
doctor of philosophy Computer Science

2015 - 2017

University of Georgia Athens, Georgia, US
Master of Science Department of Computer Science

2011 - 2015

Central South University Changsha, Hunan, CN
Bachelor of Engineering Department of Automation

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