PICom Accepted Papers (Full/Regular papers)

1. Multi-Feature Shuffle Algorithm for Root Cause Detection in Semiconductor Manufacturing - Ding Tan; Xingyu Xu; Kaixiang Yu; Sihai Zhang; Tianchi Chen
   
2. An Improved ALNS for Solving Vehicle Routing Problem with Tight Time Constraint - Tomoki Shirai
   
3. How Feature Remove and Shuffle Work in Key Feature Detection? The Perspective of NDER -Xingyu Xu; Ding Tan; Sihai Zhang; Tianchi Chen
   
4. Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection - Solomon Negussie Tesema; El-Bey Bourennane
   
5. Data-driven Adaptive Network Management with Deep Reinforcement Learning - Ameer Ivoghlian; Kevin I-Kai Wang; Zoran Salcic
   
6. Horizontal Auto-Scaling in Edge Computing Environment using Online Machine Learning - Thiago Pereira Silva; Aluizio F. Rocha, Neto; Thais Batista; Frederico Lopes; Flávia Coimbra Delicato; Paulo F. Pires
   
7. Malicious Network Traffic Detection in IoT Environments Using A Multi-level Neural Network -Menglu Li; Eleonora Achiluzzi; Md Fahd Al Georgy; Rasha Kashef
   
8. Interpretable Detection of Affective Engagement for Online Learners on Edge Devices -David Boulanger; M. Ali Akber Dewan; Vivekanandan S Kumar; Oscar Lin
   
9. Decision-Making of an Autonomous Vehicle when Approached by an Emergency Vehicle using Deep Reinforcement Learning - Hamid Shoaraee; Liang Chen; Fan Terry Jiang

PICom Accepted Papers (Work in Progress)

1. Accurate Step Count With Generalizable Deep Learning on Accelerometer Data - Long Luu; Arvind Pillai; Halsey Lea; Ruben Buendia; Faisal Khan; Glynn Dennis
   
2. Hercules: A context-aware multiple application and multisensor data fusion algorithm - Fabiano Martins; João Paixão; Claudio M. Farias; Flávia Coimbra Delicato
   
3. Network Flow Classification and Volume Prediction using Novel Ensemble Deep Learning Architectures in the Era of the Internet of Things (IoT) - Yoga Suhas Kuruba Manjunath; Vakar Kohli; Salman Ghaffar; Rasha Kashef
   
4. Towards a Hierarchical Architectural Model for IoT End-User Service Composition -Federico Montori; Vincenzo Armandi; Luca Bedogni