Fahmid Morshed Fahid
College of Engineering
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Works (9)
Updated: April 5th, 2024 12:19
2024 article
Learning to bid and rank together in recommendation systems( nov , 2023 , 10.1007/s10994-023-06444-4)
Ji, G., Jiang, W., Li, J., Fahid, F. M., Chen, Z., Li, Y., … Zhu, Z. (2024, January 4). MACHINE LEARNING.
link.springer.com (publisher PDF)
Source: Web Of Science
Added: March 11, 2024
2023 article
Learning to bid and rank together in recommendation systems
Ji, G., Jiang, W., Li, J., Fahid, F. M., Chen, Z., Li, Y., … Zhu, Z. (2023, November 29). MACHINE LEARNING.
author keywords: Recommendation systems; Contextual bandits; Policy gradients; Learning to bid; Learning to rank; Deep reinforcement learning
TL;DR:
A contextual-bandit framework to jointly optimize the price to bid for the slot and the order to rank its candidates for a given service in this type of recommendation systems, and trains reinforcement learningpolicies using deep neural networks and compute top-K Gaussian propensity scores to exclude the variance in the gradients unrelated to the reward.
(via Semantic Scholar)
Source: Web Of Science
Added: January 16, 2024
2022 journal article
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(5), 1676–1691.
author keywords: Software; Machine learning; Pattern recognition; Training; Computer hacking; Machine learning algorithms; Estimation; Technical debt; software engineering; machine learning; pattern recognition
TL;DR:
Jitterbug is proposed, a two-step framework for identifying SATDs that identifies the “easy to find” SATDs automatically with close to 100 percent precision using a novel pattern recognition technique and machine learning techniques are applied to assist human experts in manually identifying the remaining “hard to find
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 17, 2022
2022 article
Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning
ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II, Vol. 13356, pp. 218–221.
author keywords: Multimodal learning; Collaborative game-based learning; Behavioral disengagement
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 14, 2022
2022 article
Robust Adaptive Scaffolding with Inverse Reinforcement Learning-Based Reward Design
ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II, Vol. 13356, pp. 204–207.
author keywords: Inverse reinforcement learning; Reward modeling; Adaptive learning environments; Adaptive scaffolding; ICAP
Source: Web Of Science
Added: November 14, 2022
2021 article
Adaptively Scaffolding Cognitive Engagement with Batch Constrained Deep Q-Networks
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 113–124.
author keywords: Deep reinforcement learning; Cognitive engagement; ICAP; Adaptive learning environments
Source: Web Of Science
Added: November 28, 2022
2021 journal article
Assessing practitioner beliefs about software engineering
EMPIRICAL SOFTWARE ENGINEERING, 26(4).
author keywords: Software analytics; Beliefs; Productivity; Quality; Experience
TL;DR:
It is found that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day-to-day work, and programming languages act as a confounding factor for developer productivity and software quality.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: June 10, 2021
2021 article
Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II, Vol. 12749, pp. 355–360.
author keywords: Frustration trajectory; Adaptive learning environments; Problem-solving behavior; Computer science education; Block-based programming
Sources: Web Of Science, NC State University Libraries
Added: November 28, 2022
2019 article
TERMINATOR: Better Automated UI Test Case Prioritization
ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 883–894.
author keywords: automated UI testing; test case prioritization; total recall
TL;DR:
A novel TCP approach is proposed, that dynamically re-prioritizes the test cases when new failures are detected, by applying and adapting a state of the art framework from the total recall problem.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 7, 2019
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