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Tamer Nadeem <[log in to unmask]>
Wed, 22 Jul 2020 19:22:40 -0400
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****** We apologize if you receive multiple copies of this CFP *****

******* FINAL Submission Deadline: July 31, 2020 *******

Call For Papers
======================================================================================
The Second IEEE International Workshop on Harnessing the Data Revolution in
Networking
                                        (HDR-Nets 2020)
                      https://icnp20.cs.ucr.edu/hdrnets.html
                          In Conjunction with IEEE ICNP 2020
                              October 13, Madrid, Spain
======================================================================================

Artificial Intelligence (AI) and Machine Learning (ML) technologies have
achieved remarkable success nowadays in many application domains, e.g.,
natural language processing, biometrics, and computer vision. Meanwhile,
the ever-increasing complexity and scale of today’s networks keep posing
new challenges for network measurement and analytics techniques and tools.
Advances in the high-performance computing and progress in ML
methods—particularly using deep learning—have made ML/AI capable of
discovering valuable knowledge from enormous amounts of operational and
systems data. Therefore, AI/ML has been effectively used in many critical
networking data analytic functions, such as fault isolation, intrusion
detection, event correlation, log analysis, capacity planning, and design
optimization, just to name a few.

Moreover, networking has recently undergone a huge transformation enabled
by new models resulting from softwarization, virtualization, and cloud
computing. This has led to a number of novel architectures supported by
emerging technologies such as Software-Defined Networking (SDN), Network
Function Virtualization (NFV), edge computing, IoT, and 5G. On the other
hand, maturing ML techniques, such as reinforcement learning and transfer
learning, can potentially serve as a basis for incorporating learning into
automated network control. The emergence of enhanced design coupled with
the increased complexity in networking systems and protocols has fueled the
need for improved network autonomy in agile infrastructures, which can be
combined with AI/ML techniques to execute efficient, self-adaptive, rapid,
and collaborating network systems.

HDR-Nets 2020 workshop is aligned with the National Science Foundations’
(NSF) Harnessing the Data Revolution (HDR) Big Idea, a national-scale
activity to enable new modes of data-driven discovery that will allow new
fundamental questions to be addressed at the frontiers of science and
engineering, with the focus in computer and communication networks. More
specifically, HDR-Nets 2020 workshop is targeting research at the
intersection of machine learning and networking by bringing together
experts from several research communities spanning communications and
networking, machine learning, mobile computing, and big data. The agenda
will include discussions of significant contributions, community interests,
new tools, and research problems related to the design of intelligent,
robust, and adaptive communications and networks with the aid of machine
learning, as well as identifying best networking practices and design
principles for learning systems.

We encourage interdisciplinary contributions of high-quality original
technical and survey papers, which have not been published previously,
bridging the gap between machine learning, communications, and networking
from either a theoretical perspective or a practical point of view. Topics
of interest include, but not limited to, the followings:

* Machine learning, deep learning, data mining and big data analytics
algorithms for networking
* Reinforcement learning and machine learning techniques in network design,
control, scheduling, and optimization
* Energy-efficient/green network operations using machine learning and data
mining algorithms
* Machine learning for network data stream / time series analytics in
real-time
* Algorithms for dealing with data-level difficulty in networking data,
such as imbalance, noise, or high dimensionality
* Self-learning, machine learning and big data analytics  for network
intrusion/anomaly/outage/failure detection
* Natural language processing techniques for network log analytics
* Data driven network architectural and protocol design (e.g., for
vehicular networks, IoT networks)
* Learning algorithms for provisioning network resources
* Autonomous networks in DCs, WANs, IXPs, wireless networks, cloud
networks, CDNs, home networks, etc.
* Reinforcement learning and other learning techniques for virtualization
techniques including NFV, SDN, SFC, etc.
* Networking for efficient learning systems
* Machine learning at the network edge
* Federated learning and distributed networking
* New knowledge discovery theories and models from network systems data
* Data collection and visualization techniques for networks and applications
* Techniques for anonymization and user privacy protection in networking
data
* Performance analysis (e.g., security, optimality, privacy) of ML
algorithms as applied to networking problems
* Machine learning algorithms for fingerprinting network device/service
* Adversarial machine learning and networking
* Open-source AI software for networking or networked applications
* Use case applications of harnessing networking data for business
intelligence such as process optimization and vendor selection
* Use case applications of harnessing networking data for enhanced service
and user experience such as content recommendation, location-based service
and advertising
* Human factors in ML and human-ML interactions for network systems
--------------------------------


Workshop Organizers
--------------------------
- Steering Co-Chairs
  . Zhi-Li Zhang, Minnesota University, USA
- General Co-Chairs
  . Dan Pei, Tsinghua University, China
  . Anwar Walid, Nokia Bell Labs, USA
- TPC Co-Chairs
  . Harshit Chitalia, Juniper Networks, USA
  . Bartosz Krawczyk, Virginia Commonwealth University, USA
  . Tamer Nadeem, Virginia Commonwealth University, USA
- Publicity Chair
  . Hannaneh B. Pasandi, Virginia Commonwealth University, USA
- Web Chair
  . Santosh Nukavarapu, Virginia Commonwealth University, USA


Important Dates
--------------------
- Submission deadline: July 31, 2020 11:59pm EDT (Extended and Final)
- Acceptance notification: August 14, 2020
- Camera-ready due date: August 24, 2020
- Workshop date (in Conjunction with ICNP 2020): October 13, 2020


Submission Guidelines
------------------------------
All submissions must be original research not under review at any other
venue. Submissions will be evaluated on the basis of technical quality,
novelty, potential impact, and clarity. Solicited submissions include both
full technical workshop papers and white position papers. The maximum
length of such submissions is 6 pages in a two-column 10pt IEEE Computer
Society format, and if accepted they will be published by IEEE and appear
in the IEEE Xplore. Formatting for all submissions (excluding page length)
must adhere to the guidelines here:
https://icnp20.cs.ucr.edu/submission.html. In accordance with the ICNP 2020
Conference, this workshop will adapt the double-blind review policy. All
accepted papers must be presented by one of the authors.

Papers must be submitted electronically as PDF files via
https://hdrnets20.hotcrp.com.


Coronavirus update
------------------------
ICNP organizers are closely monitoring the status of the COVID-19 pandemic
and its impact on conferences and travel. We also recognize the legitimate
concerns of authors and participants regarding their own health and safety.
While we prefer to have an in-person conference/workshop to the maximum
extent possible, the decision on a particular format for the conference
will be decided later based on information available closer to the
conference dates. Regardless of the eventual format for the conference, we
will allow authors to present their accepted work remotely.

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