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"B Pasandi, Hana" <[log in to unmask]>
Wed, 15 Jul 2020 10:43:19 -0400
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****** We apologize if you receive multiple copies of this CFP *****

******* EXTENDED Submission Deadline: July 24, 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 <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 includes 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 24, 2020 11:59pm EDT (Extended)
- Acceptance notification: August 10, 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. Maximum length of such submissions is 6 pages in 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 <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 <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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