Results 181 to 190 of about 2,531,015 (212)

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions

Conference on Uncertainty in Artificial Intelligence, 2023
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community.
Karine Karine, P. Klasnja, Susan A. Murphy, Benjamin M Marlin   +3 more
semanticscholar   +1 more source

PyExplainer: Explaining the Predictions of Just-In-Time Defect Models

International Conference on Automated Software Engineering, 2021
Just-In-Time (JIT) defect prediction (i.e., an AI/ML model to predict defect-introducing commits) is proposed to help developers prioritize their limited Software Quality Assurance (SQA) resources on the most risky commits. However, the explainability of
Chanathip Pornprasit, C. Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, Patanamon Thongtanunam   +4 more
semanticscholar   +1 more source

A Just-in-Time Fine-Tuning Framework for Deep Learning of SAE in Adaptive Data-Driven Modeling of Time-Varying Industrial Processes

IEEE Sensors Journal, 2021
In modern industrial processes, soft sensors have played increasingly important roles for effective process monitoring, control and optimization. Deep learning has shown excellent ability for hierarchical nonlinear feature representation in soft sensors.
Yijun Wu, Diju Liu, Xiaofeng Yuan, Yalin Wang   +3 more
semanticscholar   +1 more source

Just‐In‐Time

Management Decision, 1987
The benefits of Just‐in‐Time techniques are clearly explained with the acquired wisdom of the Japanese experience.
Sang M. Lee, Maling Ebrahimpour
openaire   +1 more source

Using a Just‐In‐Time Approach in the Green Supply Chain, Taking Into Account CO2 Emissions, Under Uncertainty in the Pre‐ and Post‐COVID‐19 Situation

Discrete Dynamics in Nature and Society
The main objective of this study is to develop a fuzzy‐based approach for building a multistage, multiproduct, and multiperiod supply chain network (SCN) after and before the COVID‐19 pandemic.
S. Abbasi, Sobhan Jabari, Akram Mirzaei, Ashkan Azimi azad, Saba Zamanian   +4 more
semanticscholar   +1 more source

When ML Training Cuts Through Congestion: Just-in-Time Gradient Compression via Packet Trimming

ACM Workshop on Hot Topics in Networks
Distributed training of ML models generates significant network traffic when exchanging gradients and is sensitive to packet drops and retransmission caused by congestion when other traffic is sharing the network.
Xiaoqi Chen, S. Vargaftik, Ran Ben Basat
semanticscholar   +1 more source

Just-in-time scheduling problem with due windows and release dates for precast bridge girders

International Transactions in Operational Research
As the prefabrication construction method plays an increasingly important role in the construction of cross‐sea bridges, coordinating the schedule between off‐site prefabrication and on‐site assembly becomes crucial and challenging.
Gang Liu, Hongwei Wang, Yong Xie
semanticscholar   +1 more source

Just in Time

About Campus: Enriching the Student Learning Experience, 2006
A new inexpensive workforce emerges
Nancy M. Vanderpool, Joanne Risacher
openaire   +1 more source

A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection

IEEE transactions on industrial electronics (1982. Print), 2020
In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes.
Zhi-wen Chen, Chang Liu, S. Ding, Tao Peng, Chunhua Yang, W. Gui, Yuri A. W. Shardt   +6 more
semanticscholar   +1 more source

An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect Prediction

International Conference on Software Engineering, 2020
Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether software changes are defect-inducing or clean based on machine learning classifiers.
Sadia Tabassum, Leandro L. Minku, Danyi Feng, George G. Cabral, Liyan Song   +4 more
semanticscholar   +1 more source