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<h1>Artificial Intelligence of Things Workshop Program</h1>
<h1>Schedule: 8:30am - 5pm on Feb. 7th, 2020</h1>
<h1>
Location: New York Hilton Midtown Hotel<br />
1335 Avenue of the Americas<br />
New York, NY, 10019 USA
</h1>
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</div>
<!-- Opening -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>8:30 - 8:45am</h2>
<h3>Opening Remarks</h3>
</div>
</div>
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<!-- Keynote 1 - FarmBeats -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>8:45 - 9:30am</h2>
<h3>
<a data-toggle="collapse" href="#keynote" role="button" aria-expanded="false"
aria-controls="keynote">
Keynote #1 - FarmBeats: Empowering Farmers with Affordable Digital Agriculture
</a>
</h3>
<h5>Ranveer Chandra, Microsoft Azure</h5>
</div>
<div class="program_block collapse" id="keynote">
<div class="program_block_content">
<p>
Data-driven techniques help boost agricultural productivity by increasing
yields, reducing losses and cutting down input
costs. However, these techniques have seen sparse adoption owing to high costs
of manual data collection and limited
connectivity solutions. Our system, called FarmBeats, includes Cloud, IoT & AI
innovations for agriculture that enables
seamless collection and analysis of data across various sensors, cameras,
drones, and satellites. In this talk, we will
describe the system, and outline some of the AI challenges we are currently
addressing for agriculture.
</p>
<div class="author_content row">
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<p>
Ranveer Chandra is the Chief Scientist at Microsoft Azure Global. His
research has shipped in multiple Microsoft
products, including Windows, Visual Studio, XBOX, and Azure. Ranveer is
leading the
<a target="_blank" href="https://www.microsoft.com/en-us/research/project/farmbeats-iot-agriculture/">
FarmBeats
</a>,
<a target="_blank" href="https://www.microsoft.com/en-us/research/project/battery-research-at-microsoft/">
battery research
</a>, and
<a target="_blank" href="https://www.microsoft.com/en-us/research/project/dynamic-spectrum-and-tv-white-spaces/">
TV white space
</a>
research projects at Microsoft. His work on FarmBeats was
featured by Bill Gates on GatesNotes, and he has
been invited to present his research on FarmBeats to the Secretary of
Agriculture, and on TV White Spaces to the FCC
Chairman. Ranveer has published over 90 research papers, and has over
100 patents that have been granted by the USPTO.
He has won several awards, including best paper awards, and the MIT
Technology Review’s Top Innovators Under 35. Ranveer
has an undergraduate degree from IIT Kharagpur, India and a PhD from
Cornell University.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- Technical Paper Session 1 -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>9:30 - 10:30am</h2>
<h3>
Technical Paper Session #1
</h3>
<h3>
Session Chair: Dr. Jie Liu
</h3>
</div>
<div class="program_block">
<h3>
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
</h3>
<p>
Mao V. Ngo (Singapore University of Technology and Design)*; Hakima Chaouchi (CNRS,
SAMOVAR, Telecom Sud Paris, Institut Mines Telecom, Paris-Saclay University); Tie Luo
(Department of Computer Science, Missouri University of Science and Technology); Tony
Quek (Singapore University of Technology and Design)
</p>
<h3>
Applying Weak Supervision to Mobile Sensor Data: Experiences with Transport Mode
Detection
</h3>
<p>
Jonathan Fürst (NEC Laboratories Europe)*; Mauricio Fadel Argerich (NEC Laboratories
Europe); K. Shankari (UC Berkeley); Gürkan Solmaz (NEC Laboratories Europe); Bin Cheng
(NEC Laboratories Europe)
</p>
<h3>
Health-based Fault Generative Adversarial Network for Fault Diagnosis in Machine Tools
</h3>
<p> Chia-Yu Lin, Tzu-Ting Chen, Li-Chun Wang and Hong-Han Shuai (Department of Electrical and Computer Engineering, National Chiao Tung University, Taiwan)
</p>
<h3>
Accelerating Training using Tucker Decomposition
</h3>
<p>
Mostafa Elhoushi (Huawei Technologies)*; Ye Tian (Huawei Technologies); Zihao Chen
(Huawei Technologies); Farhan Shafiq (Huawei Technologies); Joey Li (Huawei
Technologies)
</p>
</div>
</div>
</div>
<!-- coffee break -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>10:30 - 11:00am</h2>
<h3>Coffee Break</h3>
</div>
</div>
</div>
<!-- Project Showcase -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>11:00 - 11:30am</h2>
<h3>Project Showcase</h3>
</div>
<div class="program_block">
<h3>
In-Car Cognition with Edge Artificial Intelligence Accelerators
</h3>
<h3>
Session Chair: Dr. Jie Liu
</h3>
<p>
Ming-Chih Lin (Microsoft)*; Sio-Sun Wong (Microsoft); Yu-Kwen Hsu (Microsoft); Ti-Hua
Yang (Microsoft); Pi-Sui Hsu
(Microsoft); He-Wei Lee (Microsoft); Wei-Chen Tsai (Microsoft); Hsiu-Tzu Wu (Microsoft);
Sung-Lin Yeh (Microsoft)
</p>
<h3>
Real-time In-store Insights Cloud-based Solution using Radar Tracking Sensors and
Demographic Analytics
</h3>
<p>
Jaeseok Kim (HCL America, Inc.)*; Hank Tsou (HCL America); Ernst Henle (HCL America);
Ashish Guttendar (HCL America)
</p>
</div>
</div>
</div>
<!-- Invited talk - Xiang Sheng -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>11:30 - 12:00pm</h2>
<h3>
<a data-toggle="collapse" href="#talk1" role="button" aria-expanded="false"
aria-controls="talk1">
Invited Talk - Sensing as a Service in the Era of AIOT
</a>
</h3>
<h5>Xiang Sheng, Facebook</h5>
</div>
<div class="program_block collapse" id="talk1">
<div class="program_block_content">
<p>
When we look back from this time point, when a new decade just started, there was a quantum leap In our ability to
know the world through data we collected from IOT devices and sharing them. Can we push the “sensing as a service”
concept to another stage? And will those efforts align with Facebook’s mission to “Give people the power to build
community and bring the world closer together.” We will cover that in our talk.
</p>
<div class="author_content row">
<div class="author_image">
<img src="./images/XiangSheng.jpg" />
</div>
<div class="author_about">
<p>
Xiang Sheng is a research scientist at Facebook research. He received his PhD from Syracuse University in Electrical
and Computer Engineering under the supervision of Dr. Jian Tang. Before joining Facebook, he worked at Microsoft,
eBay research, and Apple on applying machine learning to different areas. His current research area in Facebook
AR/VR is focusing on how to use cutting-edge technology to deliver the right experience to the right people at right
time.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- lunch -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>12:00 - 1:00pm</h2>
<h3>Lunch</h3>
</div>
</div>
</div>
<!-- Keynote 2 - Diana Marculescu -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>1:00 - 1:45pm</h2>
<h3>
<a data-toggle="collapse" href="#keynote2" role="button" aria-expanded="false"
aria-controls="keynote2">
Keynote #2 - Putting the “Machine” Back in Machine Learning: The Case for Hardware-ML Model
Co-design
</a>
</h3>
<h5>Diana Marculescu, University of Texas Austin</h5>
</div>
<div class="program_block collapse" id="keynote2">
<div class="program_block_content">
<p>
Machine learning (ML) applications have entered and impacted our lives unlike
any other technology advance from the
recent past. Indeed, almost every aspect of how we live or interact with others
relies on or uses ML for applications
ranging from image classification and object detection, to processing
multi‐modal and heterogeneous datasets. While the
holy grail for judging the quality of a ML model has largely been serving
accuracy, and only recently its resource
usage, neither of these metrics translate directly to energy efficiency,
runtime, or mobile device battery lifetime.
This talk will uncover the need for building accurate, platform‐specific power
and latency models for convolutional
neural networks (CNNs) and efficient hardware-aware CNN design methodologies,
thus allowing machine learners and
hardware designers to identify not just the best accuracy NN configuration, but
also those that satisfy given hardware
constraints.
</p>
<p>
Our proposed modeling framework is applicable to both high‐end and mobile
platforms and achieves 88.24%
accuracy for latency, 88.34% for power, and 97.21% for energy prediction. Using
similar predictive models, we
demonstrate a novel differentiable neural architecture search (NAS) framework,
dubbed Single-Path NAS, that uses one
single-path over-parameterized CNN to encode all architectural decisions based
on shared convolutional kernel
parameters. Single-Path NAS achieves state-of-the-art top-1 ImageNet accuracy
(75.62%), outperforming existing mobile
NAS methods for similar latency constraints (∼80ms) and finds the final
configuration up to 5,000× faster compared to
prior work. Combined with our quantized CNNs (Flexible Lightweight CNNs or
FLightNNs) that customize precision level in
a layer-wise fashion and achieve almost iso-accuracy at 5-10x energy reduction,
such a modeling, analysis, and
optimization framework is poised to lead to true co-design of hardware and ML
model, orders of magnitude faster than
state of the art, while satisfying both accuracy and latency or energy
constraints.
</p>
<div class="author_content row">
<div class="author_image">
<img src="./images/DMarculescu-2012-cropped.jpeg" />
</div>
<div class="author_about">
<p>
Diana Marculescu is Department Chair, Cockrell Family Chair for
Engineering Leadership #5, and Professor, Motorola
Regents Chair in Electrical and Computer Engineering #2, at the
University of Texas at Austin. Before joining UT Austin
in December 2019, she was the David Edward Schramm Professor of
Electrical and Computer Engineering, the Founding
Director of the College of Engineering Center for Faculty Success
(2015-2019) and has served as Associate Department
Head for Academic Affairs in Electrical and Computer Engineering
(2014-2018), all at Carnegie Mellon University. She
received the Dipl.Ing. degree in computer science from the Polytechnic
University of Bucharest, Bucharest, Romania
(1991), and the Ph.D. degree in computer engineering from the University
of Southern California, Los Angeles, CA (1998).
Her research interests include energy- and reliability-aware computing,
hardware aware machine learning, and computing
for sustainability and natural science applications.
</p>
<p>
Diana was a recipient of the National Science Foundation Faculty
Career Award (2000-2004), the ACM SIGDA Technical Leadership Award
(2003), the Carnegie Institute of Technology George
Tallman Ladd Research Award (2004), and several best paper awards. She
was an IEEE Circuits and Systems Society
Distinguished Lecturer (2004-2005) and the Chair of the Association for
Computing Machinery (ACM) Special Interest Group
on Design Automation (2005-2009). Diana chaired several conferences and
symposia in her area and is currently an
Associate Editor for IEEE Transactions on Computers. She was selected as
an ELATE Fellow (2013-2014), and is the
recipient of an Australian Research Council Future Fellowship
(2013-2017), the Marie R. Pistilli Women in EDA
Achievement Award (2014), and the Barbara Lazarus Award from Carnegie
Mellon University (2018). Diana is a Fellow of
both ACM and IEEE.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- Technical Paper Session 2 -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>1:45 - 2:45pm</h2>
<h3>Technical Paper Session #2</h3>
<h3>
Session Chair: Dr. Haining Zheng
</h3>
</div>
<div class="program_block">
<h3>
Block-wise Scrambled Image Recognition Using Adaptation Network
</h3>
<p>
Madono Koki (Waseda University)*; Masayuki Tanaka (Tokyo Institute of Technology); Masaki Onishi (National Institute of Advanced Industrial Science and Technology); Tetsuji Ogawa (Waseda University)
</p>
<h3>
Learning to Navigate from Synthetic Data for Friction-Adaptive Autonomous Driving
</h3>
<p>
Dai-Ying Hsieh (National Chiao Tung University); Hsiao-Han Lu (National Chiao Tung University); Ru-Fen Jheng (National Chiao Tung University); Hung-Chen Chen (National Chiao Tung University); Hong-Han Shuai (National Chiao Tung University)*; Wen-Huang Cheng (EE, NCTU)
</p>
<h3>
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry
</h3>
<p>
Nimish Awalgaonkar (School of Mechanical Engineering, Purdue University ); Haining Zheng (ExxonMobil Research and Engineering Company)*; Christopher Gurciullo (ExxonMobil Research and Engineering Company)
</p>
<h3>
Fast Image Caption Generation with Position Alignment
</h3>
<p>
Zhengcong Fei (Chinese Academy of Sciences, Institute of Computing Technology)*
</p>
</div>
</div>
</div>
<!-- Technical Paper Session 3 -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>2:45 - 3:45pm</h2>
<h3>Technical Paper Session #3</h3>
<h3>Session Chair: Dr. Haining Zheng</h3>
</div>
<div class="program_block">
<h3>
BudgetNet: Neural Network Inference under Dynamic Budget Constraints
</h3>
<p>
Anthony Chen (Dept. of Electrical Engineering, National Taiwan University); Sheng-De Wang (National Taiwan University)*
</p>
<h3>
Effective Compressed Multi-function Convolutional Neural Network Model Selection Using A New Compensatory Genetic
Algorithm-Based Method
</h3>
<p>
Luna Zhang (BigBear, Inc.)*
</p>
<h3>
Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications
</h3>
<p>
Luna Zhang (BigBear, Inc.)*
</p>
<h3>
Model Embedded DRL for Intelligent Greenhouse Control
</h3>
<p>
Tinghao Zhang (Harbin Institute of Technology)*; Jingxu Li (Harbin Institute of Technology); Jingfeng Li (Harbin Institute of Technology); ling wang (Harbin Institute of Technology); Feng Li (Harbin Institute of Technology); Jie Liu (Harbin Institute of Technology)
</p>
</div>
</div>
</div>
<!-- coffee break -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>3:45 - 4:15pm</h2>
<h3>Coffee Break</h3>
</div>
</div>
</div>
<!-- Keynote 3 - Jie Liu -->
<div class="row">
<div class="program_content_collapsing">
<div class="program_block">
<h2>4:15 - 5:00pm</h2>
<h3>
<a data-toggle="collapse" href="#keynote3" role="button" aria-expanded="false"
aria-controls="keynote3">
Keynote #3 - The Opportunities and Challenges of AIoT
</a>
</h3>
<h5>Jie Liu, Harbin Institute of Technology</h5>
</div>
<div class="program_block collapse" id="keynote3">
<div class="program_block_content">
<p>
The field of AIoT (or AI of Things) is about making real-world impact through
learning and decision making from sensor
data. Although deep-learning based AI has made amazing breakthroughs in computer
vision, natural language processing,
machine translation, and gaming, when applied to real world problems, it faces a
number of challenges from data, models,
to systems. In this talk, we use ambient intelligent environments to motivate
these challenges and present a few ideas
to address them. We will discuss how multi-modality sensor fusion can overcome
the limitation of single sensor types;
how to use simulation to generate useful training data; and how to use neural
architecture search to optimize the models
for embedded platforms. These are initial work that scratches the surface of a
complex field. We will discuss a few
future research directions towards the end.
</p>
<div class="author_content row">
<div class="author_image">
<img src="./images/JieLiu_s.jpg" />
</div>
<div class="author_about">
<p>
Jie Liu is a Chair Professor at Harbin Institute of Technology (HIT),
China and the Dean of its AI Research Institute.
Before joining HIT, he spent 18 years at Xerox PARC, Microsoft Research
and Microsoft product teams. He was a partner of
Microsoft. As a Principal Research Manager at MSR, he led the Sensing
and Energy Research Group (SERG). In MSR-NExT and
product groups, he incubated smart retail solutions, which became part
of Microsoft Business AI offering. Jie Liu’s
research interests root in understanding and managing the physical
properties of computing. He has published more than
120 peer-reviewed papers and has received 6 Best Paper Awards from top
academic conferences (h-index = 62). He has filed
more than 100 patents, with 50+ awarded.
</p>
<p>
He has chaired a number of top-tier conferences in sensing and pervasive
computing. Currently, he is the Steering Committee Chair for
Cyber-Physical Systems and Internet of Things Week (CPS-IoT
Week), Steering Committee Chair for ACM/IEEE International Conference on
Information Processing in Sensor Networks
(IPSN). He was an Associate Editor for IEEE Transactions on Mobile
Computing and ACM Transactions on Sensor Networks. He
is an IEEE Fellow and an ACM Distinguished Scientist.
</p>
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